Saving Federalism from itself will apparently require deep economic sacrifices and austerities from states, cities, towns, small businesses, and families – such is the price of Federalism…
The Capitol Building
Related Posts:
Federal Reserve Bank Smugness
Capitalism or Federalism...?
Wednesday, June 30, 2010
Tuesday, June 29, 2010
Celebrating Twelve Years in Private Practice
This year I am celebrating my twelfth year in private practice -- my plan is to go at least a dozen more...
Federal Reserve Bank Smugness
The Federal Reserve Bank of San Francisco published a strikingly smug assessment of the fiscal challenges now being confronted by states across the US:
Source: Gerst, J & Wilson, D (2010, June 28), Fiscal Crises of the States: Causes and Consequences, FRBSF Newsletter.
The current fiscal crises that most states are facing are generally the result of a severe macroeconomic downturn combined with a limited ability of the states to respond to such shocks. States are facing increased demand for public services at the same time revenue is falling. Federal stimulus support for state budgets is winding down over the next two years. Rainy-day funds are all but exhausted. Thus, state fiscal crises aren’t likely to go away soon and will probably get worse before they get better. The solutions states employ to close projected budget gaps will have painful effects on state residents and businesses but pose a more modest risk to the national recovery. Historically, the health of the national economy determines the health of state finances, not the other way around. Sustained improvement in the national economy is essential for states to grow their way out of their current problems and improve their fiscal conditions.Clearly, state governors and legislators can expect little sympathy or support from the Federal Reserve any time soon...
Source: Gerst, J & Wilson, D (2010, June 28), Fiscal Crises of the States: Causes and Consequences, FRBSF Newsletter.
Monday, June 28, 2010
Sharing Economic Hardships
Gov Arnold Schwarzenegger has proposed that all state workers in California be paid only the minimum wage until Sacramento legislators come up with a balanced budget -- interesting. Indeed, Gov Schwarzenegger's proposal may be instructive for other states or even the Federal government. Solving the nation's budget crisis would be much easier if the economic hardships were shared by everyone...
A Dire Warning...
A dire warning for the world by Prof Paul Krugman:
Source: Krugman, P (2010, June 27), The Third Depression, NY Times.
Recessions are common; depressions are rare. As far as I can tell, there were only two eras in economic history that were widely described as “depressions” at the time: the years of deflation and instability that followed the Panic of 1873 and the years of mass unemployment that followed the financial crisis of 1929-31.I tend to share in Prof Krugman’s views and concerns for the future. I maintain that monetary contraction (as in austerity measures) risks deflation and depression. Conversely, monetary expansion (as in "printing money") risks inflation and recession. Given an opportunity to choose between these two risks, I would choose monetary expansion and the risk of inflation.
Neither the Long Depression of the 19th century nor the Great Depression of the 20th was an era of nonstop decline — on the contrary, both included periods when the economy grew. But these episodes of improvement were never enough to undo the damage from the initial slump, and were followed by relapses.
We are now, I fear, in the early stages of a third depression. It will probably look more like the Long Depression than the much more severe Great Depression. But the cost — to the world economy and, above all, to the millions of lives blighted by the absence of jobs — will nonetheless be immense.
And this third depression will be primarily a failure of policy. Around the world — most recently at last weekend’s deeply discouraging G-20 meeting — governments are obsessing about inflation when the real threat is deflation, preaching the need for belt-tightening when the real problem is inadequate spending....
In the face of this grim picture, you might have expected policy makers to realize that they haven’t yet done enough to promote recovery. But no: over the last few months there has been a stunning resurgence of hard-money and balanced-budget orthodoxy....
And who will pay the price for this triumph of orthodoxy? The answer is, tens of millions of unemployed workers, many of whom will go jobless for years, and some of whom will never work again.
Source: Krugman, P (2010, June 27), The Third Depression, NY Times.
The G20 Toronto Summit Legacy
The Europeans assume that the world is going to go along with their austerity planning. However, the US cannot turn its back on California and New York the way that Germany and France can shun Greece, Spain, and Portugal. Monetary expansion in the US is immiment, whether Europeans like it or not. The European Union is blundering and may very well force the world into a deeper recession, or even depression...
Related Posts:
Depression or Inflation...?
Related Posts:
Depression or Inflation...?
Capitalism or Federalism...?
California's financial crisis will be an interesting test for Washington and the future of Federalism. Here's my question: How can Washington deny California after saying yes to General Motors, AIG, and dozens of banks?
Comments welcome...
Related Posts:
Greece: What Economic Austerity Looks Like
How Would Californians React to Economic Austerity...?
How Would New Yorkers React to Economic Austerity...?
Comments welcome...
Related Posts:
Greece: What Economic Austerity Looks Like
How Would Californians React to Economic Austerity...?
How Would New Yorkers React to Economic Austerity...?
Saturday, June 26, 2010
Threat of Another Economic Stumble Is Real
According to Dr Norman J Ornstein of the American Enterprise Institute for Public Policy Research, the global economy remains in a state of great uncertainty:
Read more
Protestors at the G20 Toronto Summit
Source: Ornstein, N J (2010, June 23), Threat of Another Economic Stumble Is Real, American Enterprise Institute.
We are nowhere near out of the woods. The danger of deflation is still there. The risk of another stumble in our economy is real, including the continuing fragility of community banks over commercial real estate loans. The global economy remains fragile, with Greece the leading edge of what could become a set of dominoes toppling in Europe as creditors become nervous and raise the premium on loans from several shaky countries, adding to their woes and endangering the euro zone and by extension the rest of us.In the mean time, the political debate over austerity measures versus monetary expansion remains as real today as it did during the 1930's...
Read more
Protestors at the G20 Toronto Summit
Source: Ornstein, N J (2010, June 23), Threat of Another Economic Stumble Is Real, American Enterprise Institute.
Friday, June 25, 2010
The Coming Inflation
As reported by Bloomberg:
Source: Robinson, E (2010, June 25), States of Crisis for 46 Governments Facing Greek-Style Deficits, Bloomberg.
Related Posts:
The United States is not the European Union
Greece: What Economic Austerity Looks Like
How Would Californians React to Economic Austerity...?
How Would New Yorkers React to Economic Austerity...?
Forty-six states face budget shortfalls that add up to $112 billion for the fiscal year ending next June, according to the Center on Budget and Policy Priorities, a Washington research institution. State spending is 12 percent of US GDP. “States are going to have to cut back spending and raise taxes the same way Greece and Spain are,” says Dean Baker, co-director of the Center for Economic and Policy Research in Washington.... State leaders won’t be able to ride out this cycle the way they have in the past. The budget holes are too large. For the first time since 1962, sales and income tax revenue fell for five straight quarters, through December 2009, according to the Nelson A Rockefeller Institute of Government at the State University of New York at Albany.... If they fail to act, state fiscal positions will steadily erode and hurt the US economy through 2060, according to a March 2010 report prepared for Congress by the US Government Accountability Office.Of course, the US cannot turn its back on its states the way that the EU can turn its back on its members. Assuming budget cuts fail at the state level (as I predict they will), the US will have no recourse but to rout government spending and indebtedness through monetary expansion and inflation. In my opinion, an inflationary economic surge is a foregone conclusion regardless of which political party is in power.
Source: Robinson, E (2010, June 25), States of Crisis for 46 Governments Facing Greek-Style Deficits, Bloomberg.
Related Posts:
The United States is not the European Union
Greece: What Economic Austerity Looks Like
How Would Californians React to Economic Austerity...?
How Would New Yorkers React to Economic Austerity...?
Thursday, June 24, 2010
The European Union is not the United States
German Chancellor Angela Merkel's recent agitations for global austerity measures should be kept in perspective by the deficit hawks in the US. For example, it is quite easy to imagine Germany turning its back on Greece in her hour of need. However, I doubt seriously that the US would be able to turn its back on California or New York should these states fall into default. The European Union is not the United States.
Germany (green) and Greece (orange)
Related Posts:
Greece: What Economic Austerity Looks Like
How Would Californians React to Economic Austerity...?
How Would New Yorkers React to Economic Austerity...?
Germany (green) and Greece (orange)
Related Posts:
Greece: What Economic Austerity Looks Like
How Would Californians React to Economic Austerity...?
How Would New Yorkers React to Economic Austerity...?
Wednesday, June 23, 2010
On Postmodernization
Prof Herman E Daly and Dr John B Cobb Jr (1994) argue that postmodernization entails “redirecting the economy for the sake of human beings and the whole biosphere” (p. 361). When combined with pluralism, postmodernization rings a bell that is hard to argue with…
Source: Daly, H E & Cobb, J B Jr (1994), For the Common Good: Redirecting the Economy toward Community, the Environment, and a Sustainable Future (2nd ed), Boston, MA: Beacon Press.
Source: Daly, H E & Cobb, J B Jr (1994), For the Common Good: Redirecting the Economy toward Community, the Environment, and a Sustainable Future (2nd ed), Boston, MA: Beacon Press.
Soros on German Austerity Planning
German Chancellor Angela Merkel unveiled austerity plans earlier this month for 80 billion euros ($107 billion) in national budget cuts over the next four years. Chancellor Merkel’s planning proposal prompted this ominous response by George Soros regarding the future of the European Union:
Source: Carrel, P & Brown, S (2010, June 23), Soros Says Germany Could Cause Euro Collapse, Reuters.
German policy is a danger for Europe; it could destroy the European project… Right now the Germans are dragging their neighbors into deflation, which threatens a long phase of stagnation. And that leads to nationalism, social unrest and xenophobia. Democracy itself could be at risk.The larger lesson here for the world is that monetary contraction and austerity carry political risks that may be worse than the risks of monetary expansion and inflation. The austerity hawks are real, as are the political risks…
Source: Carrel, P & Brown, S (2010, June 23), Soros Says Germany Could Cause Euro Collapse, Reuters.
Tuesday, June 22, 2010
The Need for Postmodernization Strategies
Given the string of calamities suffered by enterprise and society over the past several years, the need for sustainable postmodernization strategies has become increasingly evident...
PS: I stopped adding photographs after reaching a dozen...
PS: I stopped adding photographs after reaching a dozen...
Deflation or Inflation...?
I maintain that monetary contraction (as in austerity measures) risks deflation and depression. Conversely, monetary expansion (as in "printing money") risks inflation and recession. Given an opportunity to choose between these two risks, I would choose monetary expansion and the risk of inflation.
Related Posts:
Using Inflation to Reduce Public Debt and Rout Entitlements
How High Can Inflation Go...?
Repairing Sovereign Indebtedness: Get Ready
Using Inflation to Erode the US Public Debt
Implications of the Financial Crisis
Related Posts:
Using Inflation to Reduce Public Debt and Rout Entitlements
How High Can Inflation Go...?
Repairing Sovereign Indebtedness: Get Ready
Using Inflation to Erode the US Public Debt
Implications of the Financial Crisis
Monday, June 21, 2010
The Currency of the Future...?
The US dollar has been the reserve currency of choice around the world for over half a century. However, Americans might want to become more familiar with the renminbi or Chinese yuan (sign: ¥; code: CNY), the official currency of the People's Republic of China (PRC). Beijing's announcement this weekend that China would loosen the yuan's de-facto peg to the US dollar essentially means that the yuan will likely begin to increase in value vis-Ă -vis the dollar. The relationship between the yuan and dollar will be a hot topic in the coming months as the US contends with what appears to be a less than spectacular economic recovery.
Saturday, June 19, 2010
The Good Life Comes in Moments...
An oriental lily in bloom [click to enlarge]
I took this photograph this morning on my balcony here in the mountains of Pennsylvania -- the good life comes in moments...
I took this photograph this morning on my balcony here in the mountains of Pennsylvania -- the good life comes in moments...
Thursday, June 17, 2010
Monday, June 14, 2010
Comparative Economics: US and Switzerland
Is there anything that the US might learn from Switzerland about monetary and fiscal policy...?
Source: DollarDaze.org
Source: DollarDaze.org
Sunday, June 13, 2010
All the Gold at Fort Knox
According to the US Mint, the US Bullion Depository at Fort Knox currently contains approximately 5,050 tons or 147.3 million troy ounces of pure gold bullion. At the current market price of $1,226.50 per troy ounce, the gold at Fort Knox can be valued at approximately $180.7 billion.
Related Posts:
What's in Your Wallet...?
In Fiat Currency We Trust
Related Posts:
What's in Your Wallet...?
In Fiat Currency We Trust
What's in Your Wallet...?
Some say that the discipline of philosophy has failed society with regards to two incisive questions of importance in our times. The first is, What is power? The second is, What is money? The second question has been occupying my time most recently.
So, what is money, really...? To find out, the first thing I did was take a closer look at the money in my wallet, after which I concluded that money looks about like money as always looked throughout my life (though I have noticed that money now includes various security measures intended to thwart counterfeiting).
I then went to the Internet to learn more about what money is. Since I had mostly $10 "bills" in my wallet, I focused my searches on the history of the $10 bill in America. Eventually, I found several images of different $10 bills that have been issued in the US and realized that none of the images were "bills" at all. Click on the images below and see for yourself...
The first image is a so-called "Federal Reserve Note," which Americans today use on a more or less daily basis. The second image is an older "Silver Certificate," which is no longer in circulation. The third is a still older "Gold Certificate," also no longer in circulation. Now that you have carefully looked over each, ask yourself:
Which currency would you prefer to carry in your wallet...?
My preference would be the Gold Certificate, thank you very much. As for the question of what money is...
Related Posts:
In Fiat Currency We Trust
All the Gold at Fort Knox
So, what is money, really...? To find out, the first thing I did was take a closer look at the money in my wallet, after which I concluded that money looks about like money as always looked throughout my life (though I have noticed that money now includes various security measures intended to thwart counterfeiting).
I then went to the Internet to learn more about what money is. Since I had mostly $10 "bills" in my wallet, I focused my searches on the history of the $10 bill in America. Eventually, I found several images of different $10 bills that have been issued in the US and realized that none of the images were "bills" at all. Click on the images below and see for yourself...
The first image is a so-called "Federal Reserve Note," which Americans today use on a more or less daily basis. The second image is an older "Silver Certificate," which is no longer in circulation. The third is a still older "Gold Certificate," also no longer in circulation. Now that you have carefully looked over each, ask yourself:
Which currency would you prefer to carry in your wallet...?
My preference would be the Gold Certificate, thank you very much. As for the question of what money is...
Related Posts:
In Fiat Currency We Trust
All the Gold at Fort Knox
In Fiat Currency We Trust
According the the BusinessDictionary.com, a "fiat currency" is described as a:
Related Posts:
What's In Your Wallet...?
All the Gold at Fort Knox
Common type of currency issued by official order, and whose value is based on the issuing authority's guarantee to pay the stated (face) amount on demand, and not on any intrinsic worth or extrinsic backing. All national currencies in circulation, issued and managed by the respective central banks, are fiat currencies.Note the use of the terms "official" and "authority," which are legalese for "government" and "law." Most Americans place great faith in both the government and law -- so much is at stake in our political economy...
Related Posts:
What's In Your Wallet...?
All the Gold at Fort Knox
No More Money!
I saw this posting on the Internet and have to share in the sentiments:
Let’s face it — no one in America has any money — the banks are broke — the pension funds are broke — the insurance companies are broke — social security is broke — Medicare is broke — the states are broke — the cities are broke — everyone is broke.This economics stuff is easy once you put your mind to it…
The reason that no one has any money is that there is no money around — I mean, if the government wanted everyone to have more money, they would just send it to them, right?
Has anyone seen any big stacks of money recently? There’s no money to earn, to spend, to loan, to borrow, to play with, there’s simply no money anywhere — the reason for this is that the government is trying as hard as it can to get rid of money.
My children asked me why we don’t have any money, and I told them that the government has decided that good people and good children have too much money already, and so the government is working as hard as it can to get rid of money — they understood perfectly.
Saturday, June 12, 2010
Gold Accepted as Payment for Services Rendered
I was recently asked whether I accept gold as payment for services rendered in private consulting practice. The answer is, "yes."
Friday, June 11, 2010
How Much is the World Cup Worth...?
The FIFA World Cup trophy contains 11 pounds (6,175 grams) of 18 karat gold, which equates to 4,927 grams of pure gold. Today's spot price for gold is $1,226.50 per troy ounce, and since a troy ounce is equal to 31.1034768 grams, it follows that the current intrinsic value of the trophy is $194,285.85 ([4,927 / 31.1034768] * 1,226.50). However, I suspect that the various national teams now competing for the trophy in South Africa would probably pay a premium for this chunk of gold!
Source: FIFA Trophies
Source: FIFA Trophies
On Music Education in Detroit
Here are some excerpts from short essays written by students of Marc Haas, an orchestra teacher at Cass Technical High School in Detroit, whose teaching position terminates in August as the result of recent budget cuts in the Detroit schools:
Aspiring high school jazz musicians performing in Detroit
Source: Shirley, G (2010, June 9), Music Education in Detroit's Public Schools: The Struggle to Survive, New Music Box.
Aspiring high school jazz musicians performing in Detroit
"Why I Play Music"I commend Prof George Shirley's article and appeal linked below to everyone interested in music and the arts...
"Music should be in all schools, because some people do not have the freedom to express themselves. Sometimes all a child needs is to express themselves to be accepted in this world."
"Where I live is rough in the heart of the eastside there is no right path. So instead of selling drugs I practice and I practice hard whenever I'm frustrated and I feels I can't function through everyday life I play some music and my troubles melt away… I truly never thought that I would become this good at the cello…my parents love to hear me play now when at first I had to stay upstairs to play (now) they make me play for family and friends and everyone is amazed by my skill and how much I progressed. The point is that I can do anything regardless of my position. You can do it no matter what the odds because most people don't really know what it's like for me what I do when I leave Cass but I don't let it I interfere with my love for music."
"Music teaches me discipline. Music helps me establish a work ethic. Music isn't easy. It takes patience, hard work and dedication."
"Music is my way of communicating what it is that my mouth finds too hard to speak. With every aria I am able to release my inner passions. With every minuette, I am free to dance into a stupor. With every concerto I am able to let go of my anger. And with every overture I am able to overcome any and all obstacles. So when people ask why (she) plays music, she laughs and answers, 'So I can dream'."
"Music is a necessity to my life, it keeps me pushing to do better, and when you have to do that with even just one aspect of your life, it forces you to do the same with every other aspect. Why do I play music? I need to. I have to. Music is not an option."
"My parents don't tell me that they are proud of me a lot but when I play my cello for them and see the smile on their faces I know that they are proud of me for achieving something and learning how to play such a difficult piece of music."
"The people I've made memories with will be with me forever. Music brought all of us together to form this small family that has become one of the only reasons I still enjoy coming to school."
Source: Shirley, G (2010, June 9), Music Education in Detroit's Public Schools: The Struggle to Survive, New Music Box.
Thursday, June 10, 2010
Navigating Decision Analysis
Prof Gregory S Parnell has developed a nice roadmap (click graphic to expand) for navigating the art and science of decision analysis. The source article is also linked below.
Download
Source: Parnell G S (2009, May), Decision Analysis in One Chart, Decision Line, 40(3), 20-24.
Download
Source: Parnell G S (2009, May), Decision Analysis in One Chart, Decision Line, 40(3), 20-24.
Wednesday, June 09, 2010
Bankster Capitalists Beware
An Australian hedge fund has filed a complaint against Goldman Sachs in US District Court (linked below) over an investment in a subprime mortgage-linked security that contributed to the fund's demise in 2007. The complaint details how Goldman pitched the deal to the hedge fund even as the bank's sales team and mortgage traders knew the market for mortgage-linked securities would likely crumble. The complaint also alleges that a Goldman senior executive described the offering as “one shitty deal” just prior to the sale to the hedge fund. The Australian hedge fund is seeking to recoup $56 million in losses from Goldman, together with $1 billion in punitive damages. Goldman Sachs denies any wrongdoing in the case.
The complaint is yet another public relations setback not only for Goldman Sachs, but for the investment banking industry as a whole. Goldman’s alleged malfeasance and conflicts of interest continue to raise serious questions about the nature and character of investment banking as a profession. Bankster capitalists beware.
Basis Yield Alpha vs Goldman Sachs
Related Posts:
Financial Services and Banking are in Desperate Need of Reform at the Top
The complaint is yet another public relations setback not only for Goldman Sachs, but for the investment banking industry as a whole. Goldman’s alleged malfeasance and conflicts of interest continue to raise serious questions about the nature and character of investment banking as a profession. Bankster capitalists beware.
Basis Yield Alpha vs Goldman Sachs
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Financial Services and Banking are in Desperate Need of Reform at the Top
Tuesday, June 08, 2010
Embedded versus Embodied Decision Support
Within the realm of decision support methodologies, two very different paradigms are vying for the attention of enterprise. The first is what I call the embedded approach to decision support. The embedded approach to decision support emphasizes scientific logic and rigor, and is grounded firmly in the traditional disciplines of operations research, systems analysis, decision analysis, and risk analysis[1].
Embedded Decision Support Methodologies
The second still emerging approach is what I call the embodied approach to decision support. The embodied approach to decision support traces its roots to the information technology movement and enjoys critical acclaim for its potential for automated performance monitoring, business intelligence, and business analytics.
Embodied Decision Support Methodologies
Note that the content-validity of the embedded approach is widely accepted amongst professional researchers and analysts as a body of knowledge. The literature underlying the embedded approach is vast and rich in empirical evidence supporting the validity and reliability of its methods. This extant literature regarding the embedded approach is synonymous with the disciplines of operations research, systems analysis, decision analysis, and risk analysis.
The content-validity of the still emerging embodied approach remains in question. Researchers and analysts are still debating many of the terms used in the embodied approach, and a broad consensus regarding what exactly business intelligence and business analytics entail is not yet evident. The existing literature supporting the effectiveness of embodied methods is mostly descriptive with scant empirical evidence to support its validity and reliability as a proven decision support methodology.
The significance of the differentiation between embedded and embodied methods lies in the warranties that each provide the decision maker. An impressive quality of the embedded approach is that all the terms and concepts used are clearly defined and widely accepted by professional researchers and analysts thus enabling users to articulate universally their findings and recommendations.
In contrast, the lack of consensus regarding the validity and reliability of embodied methodologies limits the utility of what we know to be business intelligence and business analytics. Indeed, the methodological frameworks for business intelligence and business analytics are still emerging in the form of dashboard reporting systems and other untested visualization methods that some researchers argue can lead to cognitive distortions of the evidence uncovered by such methods.
Future consilience between the practitioners of embedded and embodied methods is far from complete or even certain. More empirical evidence will be needed before the embodied approach can be fully converged or enjoined within the deeper conceptual foundations of embedded methodologies. In the mean time, decision makers are advised to take precautions to ensure that embedded methodologies take the lead in verifying and confirming the findings and recommendations of embodied technologies.
[1] Note that risk analysis is not to be confused with risk management, which is a different function and discipline all together.
Embedded Decision Support Methodologies
The second still emerging approach is what I call the embodied approach to decision support. The embodied approach to decision support traces its roots to the information technology movement and enjoys critical acclaim for its potential for automated performance monitoring, business intelligence, and business analytics.
Embodied Decision Support Methodologies
Note that the content-validity of the embedded approach is widely accepted amongst professional researchers and analysts as a body of knowledge. The literature underlying the embedded approach is vast and rich in empirical evidence supporting the validity and reliability of its methods. This extant literature regarding the embedded approach is synonymous with the disciplines of operations research, systems analysis, decision analysis, and risk analysis.
The content-validity of the still emerging embodied approach remains in question. Researchers and analysts are still debating many of the terms used in the embodied approach, and a broad consensus regarding what exactly business intelligence and business analytics entail is not yet evident. The existing literature supporting the effectiveness of embodied methods is mostly descriptive with scant empirical evidence to support its validity and reliability as a proven decision support methodology.
The significance of the differentiation between embedded and embodied methods lies in the warranties that each provide the decision maker. An impressive quality of the embedded approach is that all the terms and concepts used are clearly defined and widely accepted by professional researchers and analysts thus enabling users to articulate universally their findings and recommendations.
In contrast, the lack of consensus regarding the validity and reliability of embodied methodologies limits the utility of what we know to be business intelligence and business analytics. Indeed, the methodological frameworks for business intelligence and business analytics are still emerging in the form of dashboard reporting systems and other untested visualization methods that some researchers argue can lead to cognitive distortions of the evidence uncovered by such methods.
Future consilience between the practitioners of embedded and embodied methods is far from complete or even certain. More empirical evidence will be needed before the embodied approach can be fully converged or enjoined within the deeper conceptual foundations of embedded methodologies. In the mean time, decision makers are advised to take precautions to ensure that embedded methodologies take the lead in verifying and confirming the findings and recommendations of embodied technologies.
[1] Note that risk analysis is not to be confused with risk management, which is a different function and discipline all together.
Monday, June 07, 2010
Decision Warranties
According to Prof Ronald A Howard (1992):
Source: Howard, R A (1992), Heathens, Heretics, and Cults, Interfaces, 22(6), 15-27.
Three of the warranties that I would like to have in any decision situation are that:Plain and simple...
- The decision approach I am using has all the terms and concepts used so clearly defined that I know both what I am talking about and what I am saying about it;
- I can readily interpret the results of the approach to see clearly the implications of choosing any alternative, including of course, the best one; and
- The procedure used to arrive at the recommendations does not violate the rules of logic (common sense).
Source: Howard, R A (1992), Heathens, Heretics, and Cults, Interfaces, 22(6), 15-27.
Sunday, June 06, 2010
The Evolution of Decision Analysis
by Ronald A Howard © The Stanford Decisions and Ethics Center
Although decision analysis has developed significantly over the last two decades, the basic principles of the field have served well. They are unlikely to change because they are based on simple logic. In the first part of this paper, we summarize the original, fundamental disciplines of decision analysis; in the second part, we show how the discipline has evolved.
PART I: A BRIEF DESCRIPTION OF DECISION ANALYSIS
Making important decisions often requires treating major uncertainty, long time horizons, and complex value issues. To deal with such problems, the discipline of decision analysis was developed. The discipline comprises the philosophy, theory, methodology, and professional practice necessary to formalize the analysis of important decisions.
Overview of Decision Analysis
Decision analysis is the latest step in a sequence of quantitative advances in the operations research/management science field. Specifically, decision analysis results from combining the fields of systems analysis and statistical decision theory. Systems analysis, which grew as a branch of engineering, was good at capturing the interactions and dynamic behavior of complex situations. Statistical decision theory was concerned with logical decisions in simple, uncertain situations. The merger of these concepts creates a methodology for making logical decisions in complex, dynamic, and uncertain situations.
Decision analysis specifies the alternatives, information, and preferences of the decision-maker and then finds the logically implied decision.
Decision-making requires choosing between alternatives, mutually exclusive resource allocations that will produce outcomes of different desirabilities with different likelihoods. While the range of alternatives to be considered is set by the decision-maker, the decision analyst may be able to suggest new alternatives as the analysis progresses.
Since uncertainty is at the heart of most significant decision problems, decision-making requires specifying the amount of uncertainty that exists given available information. Many decision problems become relatively trivial if uncertainty is removed. For example, consider how easily a decision-maker could make a critical decision in launching a new commercial product if he could predict with certainty production and sales costs, price-demand relationships, and governmental decisions. Decision analysis treats uncertainty effectively by encoding informed judgment in the form of probability assignments to events and variables.
Decision-making also requires assigning values on the outcomes of interest to the decision-maker. These outcomes may be as customary as profit or as troubling as pain. Decision analysis determines the decision-maker's trade-offs between monetary and non-monetary outcomes and also establishes in quantitative terms his preferences for outcomes that are risky or distributed over time.
One of the most basic concepts in decision analysis is the distinction between a good decision and a good outcome. A good decision is a logical decision -- one based on the information, values, and preferences of the decision-maker. A good outcome is one that is profitable, or otherwise highly valued. In short, a good outcome is one that we wish would happen. By making good decisions in all situations that face us, we hope to ensure as high a percentage of good outcomes as possible. We may be disappointed to find that a good decision has produced a bad outcome, or dismayed to learn that someone who has made what we consider to be a bad decision has achieved a good outcome. Short of having a clairvoyant, however, making good decisions is the best way to pursue good outcomes.
An important benefit of decision analysis is that it provides a formal, unequivocal language for communication among the people included in the decision-making process. During the analysis, the basis for a decision becomes evident, not just the decision itself. A disagreement about whether to adopt an alternative may occur because individuals possess different relevant information or because they place different values on the consequences. The formal logic of decision analysis subjects these component elements of the decision process to scrutiny. Information gaps can be uncovered and filled, and differences in values can be openly examined. Revealing the sources of disagreement usually opens the door to cooperative resolution.
The formalism of decision analysis is also valuable for vertical communication in a management hierarchy. The organizational value structure determined by policymakers must be wedded to the detailed information that the line manager, staff analyst, or research worker possesses. By providing a structure for delegating decision-making to lower levels of authority and for synthesizing information from diverse areas for decision-making at high levels, decision analysis accomplishes this union.
Methodology
The application of decision analysis often takes the form of an iterative procedure called the Decision Analysis Cycle (see Figure 1). Although this procedure is not an inviolable method of attacking the problem, it is a means of ensuring that essential steps have been considered.
The procedure is divided into three phases. In the first (deterministic) phase, the variables affecting the decision are defined and related, values are assigned, and the importance of the variables is measured without any consideration of uncertainty.
The second (probabilistic) phase starts with the encoding of probability on the important variables; then, the associated probability assignments on values are derived. This phase also introduces the assessment of risk preference, which defines the best solution in the face of uncertainty.
In the third (informational) phase, the results of the first two phases are reviewed to determine the economic value of eliminating uncertainty in each of the important variables in the problem. In some ways, this is the most important phase because it shows just what it would be worth in dollars and cents to have perfect information. Comparing the value of information with its cost determines whether additional information should be collected.
If there are further profitable sources of information, then the decision should be to gather the information rather than to make the primary decision at this time. The design and execution of the information-gathering program follows.
Since new information generally requires revisions in the original analysis, the original three phases must be performed once more. However, the additional work required to incorporate the modifications is usually slight, and the evaluation, rapid. At the decision point, it may again be profitable to gather new information and repeat the cycle, or it may be more advisable to act. Eventually, the decision to act will be made because the value of new analysis and information-gathering will be less than its cost.
Applying the above procedure ensures that the total effort is responsive to changes in information -- the approach is adaptive. Identifying the crucial areas of uncertainty can also aid in generating new alternatives for future analysis.
Model Sequence
Typically, a decision analysis is performed not with one, but with a sequence of progressively more realistic models. These models generally will be in the form of computer programs. The first model in the sequence is the pilot model, an extremely simplified representation of the problem useful only for determining the most important relationships. Although the pilot model looks very little like the desired final product, it is indispensable in achieving that goal.
The next model in the sequence is the prototype model, a quite detailed representation of the problem that may, however, still be lacking a few important attributes. Although it will generally have objectionable features that must be eliminated, it does demonstrate how the final version will appear and perform.
The final model in the sequence is the production model; it is the most accurate representation of reality that decision analysis can produce. It should function well even though it may retain features that are treated in a less than ideal way.
Starting with the pilot model, sensitivity analyses are used throughout each phase to guide its further evolution. If decisions are insensitive to changes in some aspect of the model, there is no need to model that particular aspect in more detail. The goal of a good modeler is to model in detail only those aspects of the problem that have an impact on the decisions, while keeping the costs of this modeling commensurate with the level of the overall analysis.
Important aids in determining whether further modeling is economically justifiable are the calculations of the value of information. Some variables may be uncertain partially because detailed models have not bee" constructed. If the analyst can calculate the value of perfect information about these variables, he will have a standard to use in comparing the co of any additional modeling. If the cost of modeling is greater than the value of perfect information, the modeling is clearly not economically justifiable.
Using a combination of sensitivity analysis and calculations of the value of information, the analyst continually directs the development of model in an economically efficient way. An analysis conducted in this wa provides not only answers, but also often insights for creating new alternatives. When completed, the model should be able to withstand the test of any good engineering design: additional modeling resources could utilized with equal effectiveness in any part of the model. There is no such thing as a final or complete analysis; there is only an economic analysis given the resources available.
PART II: REFINEMENTS AND NEW DEVELOPMENTS IN DECISION ANALYSIS
Having seen the basic concepts of decision analysis and the main poi of its professional practice, let us now examine some of the evolution changes in the field over the last two decades.
The Decision Basis
It has become useful to have a name for the formal description of a decision problem; we call it the decision basis. The decision basis consists of a quantitative specification of the three elements of the basis: the alternatives, the information, and the preferences of the decision-maker. We can then think of two essential steps in any decision analysis: the development and the evaluation of the decision basis.
Basis Development
To develop the decision basis, the decision analyst must elicit each of the three elements from the decision-maker or from his delegates. For example, in a medical problem, the ultimate decision-maker should be the patient. The patient would provide the element of preference in the basis, probably in a series of interviews with the decision analyst. In most cases, however, the patient will delegate the alternative and information elements to doctors who, in turn, would be interviewed by the decision analyst. The analyst should be able to certify that the decision basis accurately represents the alternatives, information, and preferences provided directly or indirectly by the decision-maker. We should note here that the alternatives must include alternatives of information-gathering, such as tests, experimental programs, surveys, or pilot plants.
One key issue is the extent to which the decision analyst can provide substantive portions of the decision basis by acting as an expert. In many circumstances, the analyst cannot be an expert because he has only a lay knowledge of the decision field. Even when the analyst does have substantial knowledge of the subject area, he should make clear to the decision-maker when he has changed from the role of decision analyst to that substantive expert. Playing the role of expert can also force the analyst to defend his views against those of others; to this extent, he would be less of a "fair witness" in the subsequent analysis. Nevertheless, this possible loss of impartiality and fresh viewpoint must be balanced against the communication advantages of dealing with an analyst familiar with the decision field.
Basis Evaluation
Once the basis is developed, the next step is to evaluate it using the sensitivity analysis and value of information calculations described earlier. However, casting the problem as a decision basis shows that value-of-information calculations, important as they are, focus on only one element of the basis -- information.
Using the concept of the basis, we can also compute the value of a new alternative, which we might call the value of control. Such a calculation might well motivate the search for an alternative with certain characteristics and perhaps even the development of such an alternative.
One can perform a similar sensitivity analysis to preference with the intention not of changing preference, but of ensuring that preferences have been accurately assessed. A large change in value resulting from a small change in preference would indicate the need for more interviews about preference.
A Revised Cycle
Using the concept of the basis, we may wish to restructure the decision analysis cycle in the four-phase form shown in Figure 2. Here, the information gathering that must precede analysis or augment subsequent analyses has been included in a basis development phase. The deterministic and probabilistic phases are essentially unchanged, but the informational phase -- renamed "basis appraisal" -- is expanded to include the examination of all three basis elements.
A Refined Analysis Sequence
As a problem is analyzed, the analysis may progress through the decision analysis cycle several times in increasing levels of detail. The basic distinction is between the pilot and full-scale analysis. The pilot analysis is a simplified, approximate, but comprehensive, analysis of a decision problem. The dictionary defines pilot as "serving as a tentative model for future experiment or development." The full-scale analysis is an increasingly realistic, accurate, and justifiable analysis of a decisicision problem, where full-scale is defined as "employing all resources, not limited or partial." To understand these distinctions, we must explain in more detail what constitutes a pilot or full-scale analysis.
The purpose of a pilot analysis is to provide understanding and establish effective communication about the nature of the decision and the major issues surrounding it. The content of the pilot analysis is a simplified decision model, a tentative preference structure, and a rough characterization of uncertainty. From a pilot analysis, the decision-maker should expect preliminary recommendations for the decision and the analyst should expect guidance in conducting the full-scale analysis.
The purpose of the full-scale analysis is to find the most desirable action, given the fully developed decision basis. The full-scale analysis consists of a balanced and realistic decision model, preferences that have been certified by the decision-maker, and a careful representation of important uncertainties. From the full-scale analysis, the decision-maker should expect a recommended course of action.
While most analyses progress from pilot to full-scale, some are so complex that valuable distinctions may be made between different stages of full-scale analysis.
The first stage of full-scale analysis is the prototypical stage, which is intended to reveal weaknesses and excesses in the full-scale analysis that are worthy of correction. A prototype is defined as "an original type, form, or instance that serves as a model on which later stages are based or judged."
After the indicated corrections have been made, the analyst has an integrated stage of full-scale analysis that provides the decision-maker with confidence in having a unified, balanced, and economic analysis as a basis for decision. To integrate is "to make into a whole by bringing all parts together: unify." If a decision-maker is making a personal decision that will not require the support or approval of others, then the integrated stage of full-scale analysis is all that is required. However, if the decision-maker must convince others of the wisdom of the chosen course of action or even defend that course against hostile elements, then an additional stage of full-scale analysis will be necessary — the defensible stage.
The defensible stage of full-scale analysis is intended to demonstrate to supportive, doubtful, and possibly hostile audiences that the analysis provides an appropriate basis for decision. Defensible means "capable of being defended, protected, or justified." Typically, defensible analyses are necessary for important decisions in the public arena; however, even private enterprises may wish to conduct defensible analyses to win the support of workers, financial institutions, or venture partners. Defensible analyses are very demanding because they must show not only that the basis used is reasonable, but also that other possible bases that would lead to different decisions are not reasonable.
Contributions from Psychological Research
One of the most significant factors influencing the practice of decision analysis in recent years has been new knowledge about cognitive processes from the field of psychology. This research, centering on the contribution of Kahneman and Tversky, has had two major effects. First, the research on cognitive biases [10] has shown the need for subtlety and careful procedure in eliciting the probabilistic judgments on which decision analysis depends. Second, and perhaps even more important the descriptive research on how people actually make decisions [6,11] shows that man is considerably less skilled in decision-making than expected. The main thrust of this research shows that people violate the rules of probabilistic logic in even quite simple settings. When we say that people violate certain rules, we mean that when they are made aware of the implications of their choices, they often wish they had made another choice: that is, they realize they have made a mistake. While these mistakes can be produced in analyzing simple decision settings, they become almost unavoidable when the problem is complex.
These findings may change our interpretation of the logical axioms that are the foundations of decision analysis. We have always considered these axioms as normative: they must be satisfied if our decisions are to have many properties that we would regard as desirable. If a particular individual did not satisfy the axioms, then he would be simply making mistakes in the view of those who followed the axioms. While this interpretation is still possible, a more appropriate way to look at the axioms is that they describe what any person would do if faced with a situation as simple as the one described by the axioms. In other words, the axioms are descriptive of human behavior for simple situations. If, however, the situation becomes more complex, more "opaque" as opposed to "transparent," the axioms are no longer descriptive because the person may unintentionally violate the axiom systems.
We may now think of the job of the decision analyst as that of making "opaque" situations "transparent," so that the person clearly sees what t do. This interpretation of the work may not make it any easier, but it is far more humane than the view that the analyst is trying to impose logic willfully illogical world.
Influence Diagrams
The influence diagram is one of the most useful concepts developed in decision analysis [3]. The analyst has always faced the problem of how to reduce the multifaceted knowledge in people's heads to a form that could meet the rigid tests of explicitness and consistency required by a computer. The analyst has always faced the problem of how to reduce the multifaceted knowledge in people's heads to a form that could meet the rigid tests of explicitness and consistency required by a computer. The influence diagram is a major aid in this transformation because it cross the border between the graphic view of relationships that is very convenient for human beings and the explicit equations and numbers that are the province of present computers. To find a device that can readily be sketched by a layman and yet be so carefully defined that useful theorems concerning it can be proved by formal methods is rare. Although there is a danger that people who do not thoroughly understand influence diagrams may abuse them and be misled, there is an even greater promise that the influence diagram will be an important bridge between analyst and decision-maker.
Valuing Extreme Outcomes
One of the problems perplexing early users of decision analysis was how to treat outcomes so extreme that they seemed to be beyond analysis. For example, the question of how a person's death as the result of medical treatment can be balanced with other medical outcomes, like paralysis or even purely economic outcomes, was especially demanding. These problems appear to raise both ethical dilemmas and technical difficulties. One ethical dilemma centered on who had the right to value lives. A technical difficulty was revealed when an economist testifying in court on the value of a life was asked whether he would be willing to allow himself to be killed if he were given that amount of money. Nevertheless, once the ethical issue is clarified by acknowledging that a person may properly place a value on his own life, then the technical question of how to do it can be addressed quite satisfactorily, especially in the case of exposure to the many small risks present in modern life [4,5]. The results have major implications for many decisions affecting health and safety.
The development of ways to think about the unthinkable has shown that no decision problem lies beyond the realm of decision analysis. That is very satisfying, for were you faced with medical decisions about a loved one, would you want to use second-rate logic any more than a second-rate doctor?
Conclusion
When decision analysis was first developed, a common comment was, "If this is such a great idea, why doesn't [insert name of large, famous company] use it?" Today, it is difficult to find a major corporation that has not employed decision analysis in some form. There are some factors that should lead to even greater use. For example, decision analysis procedures are now more efficiently executable because the increased power of modern computers has reduced the costs of even very complex analyses to an affordable level. The problems that can be successfully attacked now run the gamut of all important decision problems. Increasing uncertainties and rapid change require fresh solutions rather than tested "rules of thumb." Some day, decision analysis of important decisions will perhaps become recognized as so necessary for conducting a provident life that it will be taught in grade school rather than in graduate school.
References:
1. Ronald A Howard, "Decision Analysis: Applied Decision Theory," Proceedings of the Fourth International Conference on Operational Research, Wiley-Interscience, New York, 1966, pp. 55-71.
2. Ronald A Howard, "The Foundations of Decision Analysis," IEEE Transactions on Systems Science and Cybernetics, SSC-4, No. 3, (September 1968): 211-19.
3. Ronald A Howard and James E Matheson, "Influence Diagrams," Department of Engineering-Economic Systems, Stanford University, July 1979.
4. Ronald A Howard, "On Making Life and Death Decisions," Societal Risk Assessment, How Safe Is Safe Enough?, Edited by Richard C. Schwing and Walter A Albers, Jr, General Motors Research Laboratories, Plenum Press, New York, 1980.
5. Ronald A Howard, James E Matheson, and Daniel L Owen, "The Value of Life and Nuclear Design," Proceedings of the American Nuclear Society Topical Meeting on Probabilistic Analysis of Nuclear Reactor Safety, American Nuclear Society, May 8-10, 1978.
6. Daniel Kahneman and Amos Tversky, "Prospect Theory: An Analysis of Decision under Risk," Econometrica, 47, No. 2 (March 1979): 263-291.
7. D Warner North, "A Tutorial Introduction to Decision Theory," IEEE Transactions on Systems Science and Cybernetics, SSC-4, No 3, (September 1968): 200-10.
8. Howard Raiffa, Decision Analysis: Introductory Lectures on Choices under Uncertainty, Addison-Wesley, 1968.
9. Myron Tribus, Rational Descriptions, Decisions, and Designs, Pergamon Press, 1969.
10. Amos Tversky and Daniel Kahneman, "Judgment under Uncertainty: Heuristics and Biases," Science, 185 (Sept 27, 1974): 1124-1131.
11. Amos Tversky and Daniel Kahneman, "The Framing of Decisions and the Psychology of Choice," Science, 211 (Jan 30, 1981): 453-458.
Republished with permission of The Stanford Decisions and Ethics Center
Although decision analysis has developed significantly over the last two decades, the basic principles of the field have served well. They are unlikely to change because they are based on simple logic. In the first part of this paper, we summarize the original, fundamental disciplines of decision analysis; in the second part, we show how the discipline has evolved.
PART I: A BRIEF DESCRIPTION OF DECISION ANALYSIS
Making important decisions often requires treating major uncertainty, long time horizons, and complex value issues. To deal with such problems, the discipline of decision analysis was developed. The discipline comprises the philosophy, theory, methodology, and professional practice necessary to formalize the analysis of important decisions.
Overview of Decision Analysis
Decision analysis is the latest step in a sequence of quantitative advances in the operations research/management science field. Specifically, decision analysis results from combining the fields of systems analysis and statistical decision theory. Systems analysis, which grew as a branch of engineering, was good at capturing the interactions and dynamic behavior of complex situations. Statistical decision theory was concerned with logical decisions in simple, uncertain situations. The merger of these concepts creates a methodology for making logical decisions in complex, dynamic, and uncertain situations.
Decision analysis specifies the alternatives, information, and preferences of the decision-maker and then finds the logically implied decision.
Decision-making requires choosing between alternatives, mutually exclusive resource allocations that will produce outcomes of different desirabilities with different likelihoods. While the range of alternatives to be considered is set by the decision-maker, the decision analyst may be able to suggest new alternatives as the analysis progresses.
Since uncertainty is at the heart of most significant decision problems, decision-making requires specifying the amount of uncertainty that exists given available information. Many decision problems become relatively trivial if uncertainty is removed. For example, consider how easily a decision-maker could make a critical decision in launching a new commercial product if he could predict with certainty production and sales costs, price-demand relationships, and governmental decisions. Decision analysis treats uncertainty effectively by encoding informed judgment in the form of probability assignments to events and variables.
Decision-making also requires assigning values on the outcomes of interest to the decision-maker. These outcomes may be as customary as profit or as troubling as pain. Decision analysis determines the decision-maker's trade-offs between monetary and non-monetary outcomes and also establishes in quantitative terms his preferences for outcomes that are risky or distributed over time.
One of the most basic concepts in decision analysis is the distinction between a good decision and a good outcome. A good decision is a logical decision -- one based on the information, values, and preferences of the decision-maker. A good outcome is one that is profitable, or otherwise highly valued. In short, a good outcome is one that we wish would happen. By making good decisions in all situations that face us, we hope to ensure as high a percentage of good outcomes as possible. We may be disappointed to find that a good decision has produced a bad outcome, or dismayed to learn that someone who has made what we consider to be a bad decision has achieved a good outcome. Short of having a clairvoyant, however, making good decisions is the best way to pursue good outcomes.
An important benefit of decision analysis is that it provides a formal, unequivocal language for communication among the people included in the decision-making process. During the analysis, the basis for a decision becomes evident, not just the decision itself. A disagreement about whether to adopt an alternative may occur because individuals possess different relevant information or because they place different values on the consequences. The formal logic of decision analysis subjects these component elements of the decision process to scrutiny. Information gaps can be uncovered and filled, and differences in values can be openly examined. Revealing the sources of disagreement usually opens the door to cooperative resolution.
The formalism of decision analysis is also valuable for vertical communication in a management hierarchy. The organizational value structure determined by policymakers must be wedded to the detailed information that the line manager, staff analyst, or research worker possesses. By providing a structure for delegating decision-making to lower levels of authority and for synthesizing information from diverse areas for decision-making at high levels, decision analysis accomplishes this union.
Methodology
The application of decision analysis often takes the form of an iterative procedure called the Decision Analysis Cycle (see Figure 1). Although this procedure is not an inviolable method of attacking the problem, it is a means of ensuring that essential steps have been considered.
The procedure is divided into three phases. In the first (deterministic) phase, the variables affecting the decision are defined and related, values are assigned, and the importance of the variables is measured without any consideration of uncertainty.
The second (probabilistic) phase starts with the encoding of probability on the important variables; then, the associated probability assignments on values are derived. This phase also introduces the assessment of risk preference, which defines the best solution in the face of uncertainty.
In the third (informational) phase, the results of the first two phases are reviewed to determine the economic value of eliminating uncertainty in each of the important variables in the problem. In some ways, this is the most important phase because it shows just what it would be worth in dollars and cents to have perfect information. Comparing the value of information with its cost determines whether additional information should be collected.
If there are further profitable sources of information, then the decision should be to gather the information rather than to make the primary decision at this time. The design and execution of the information-gathering program follows.
Since new information generally requires revisions in the original analysis, the original three phases must be performed once more. However, the additional work required to incorporate the modifications is usually slight, and the evaluation, rapid. At the decision point, it may again be profitable to gather new information and repeat the cycle, or it may be more advisable to act. Eventually, the decision to act will be made because the value of new analysis and information-gathering will be less than its cost.
Applying the above procedure ensures that the total effort is responsive to changes in information -- the approach is adaptive. Identifying the crucial areas of uncertainty can also aid in generating new alternatives for future analysis.
Model Sequence
Typically, a decision analysis is performed not with one, but with a sequence of progressively more realistic models. These models generally will be in the form of computer programs. The first model in the sequence is the pilot model, an extremely simplified representation of the problem useful only for determining the most important relationships. Although the pilot model looks very little like the desired final product, it is indispensable in achieving that goal.
The next model in the sequence is the prototype model, a quite detailed representation of the problem that may, however, still be lacking a few important attributes. Although it will generally have objectionable features that must be eliminated, it does demonstrate how the final version will appear and perform.
The final model in the sequence is the production model; it is the most accurate representation of reality that decision analysis can produce. It should function well even though it may retain features that are treated in a less than ideal way.
Starting with the pilot model, sensitivity analyses are used throughout each phase to guide its further evolution. If decisions are insensitive to changes in some aspect of the model, there is no need to model that particular aspect in more detail. The goal of a good modeler is to model in detail only those aspects of the problem that have an impact on the decisions, while keeping the costs of this modeling commensurate with the level of the overall analysis.
Important aids in determining whether further modeling is economically justifiable are the calculations of the value of information. Some variables may be uncertain partially because detailed models have not bee" constructed. If the analyst can calculate the value of perfect information about these variables, he will have a standard to use in comparing the co of any additional modeling. If the cost of modeling is greater than the value of perfect information, the modeling is clearly not economically justifiable.
Using a combination of sensitivity analysis and calculations of the value of information, the analyst continually directs the development of model in an economically efficient way. An analysis conducted in this wa provides not only answers, but also often insights for creating new alternatives. When completed, the model should be able to withstand the test of any good engineering design: additional modeling resources could utilized with equal effectiveness in any part of the model. There is no such thing as a final or complete analysis; there is only an economic analysis given the resources available.
PART II: REFINEMENTS AND NEW DEVELOPMENTS IN DECISION ANALYSIS
Having seen the basic concepts of decision analysis and the main poi of its professional practice, let us now examine some of the evolution changes in the field over the last two decades.
The Decision Basis
It has become useful to have a name for the formal description of a decision problem; we call it the decision basis. The decision basis consists of a quantitative specification of the three elements of the basis: the alternatives, the information, and the preferences of the decision-maker. We can then think of two essential steps in any decision analysis: the development and the evaluation of the decision basis.
Basis Development
To develop the decision basis, the decision analyst must elicit each of the three elements from the decision-maker or from his delegates. For example, in a medical problem, the ultimate decision-maker should be the patient. The patient would provide the element of preference in the basis, probably in a series of interviews with the decision analyst. In most cases, however, the patient will delegate the alternative and information elements to doctors who, in turn, would be interviewed by the decision analyst. The analyst should be able to certify that the decision basis accurately represents the alternatives, information, and preferences provided directly or indirectly by the decision-maker. We should note here that the alternatives must include alternatives of information-gathering, such as tests, experimental programs, surveys, or pilot plants.
One key issue is the extent to which the decision analyst can provide substantive portions of the decision basis by acting as an expert. In many circumstances, the analyst cannot be an expert because he has only a lay knowledge of the decision field. Even when the analyst does have substantial knowledge of the subject area, he should make clear to the decision-maker when he has changed from the role of decision analyst to that substantive expert. Playing the role of expert can also force the analyst to defend his views against those of others; to this extent, he would be less of a "fair witness" in the subsequent analysis. Nevertheless, this possible loss of impartiality and fresh viewpoint must be balanced against the communication advantages of dealing with an analyst familiar with the decision field.
Basis Evaluation
Once the basis is developed, the next step is to evaluate it using the sensitivity analysis and value of information calculations described earlier. However, casting the problem as a decision basis shows that value-of-information calculations, important as they are, focus on only one element of the basis -- information.
Using the concept of the basis, we can also compute the value of a new alternative, which we might call the value of control. Such a calculation might well motivate the search for an alternative with certain characteristics and perhaps even the development of such an alternative.
One can perform a similar sensitivity analysis to preference with the intention not of changing preference, but of ensuring that preferences have been accurately assessed. A large change in value resulting from a small change in preference would indicate the need for more interviews about preference.
A Revised Cycle
Using the concept of the basis, we may wish to restructure the decision analysis cycle in the four-phase form shown in Figure 2. Here, the information gathering that must precede analysis or augment subsequent analyses has been included in a basis development phase. The deterministic and probabilistic phases are essentially unchanged, but the informational phase -- renamed "basis appraisal" -- is expanded to include the examination of all three basis elements.
A Refined Analysis Sequence
As a problem is analyzed, the analysis may progress through the decision analysis cycle several times in increasing levels of detail. The basic distinction is between the pilot and full-scale analysis. The pilot analysis is a simplified, approximate, but comprehensive, analysis of a decision problem. The dictionary defines pilot as "serving as a tentative model for future experiment or development." The full-scale analysis is an increasingly realistic, accurate, and justifiable analysis of a decisicision problem, where full-scale is defined as "employing all resources, not limited or partial." To understand these distinctions, we must explain in more detail what constitutes a pilot or full-scale analysis.
The purpose of a pilot analysis is to provide understanding and establish effective communication about the nature of the decision and the major issues surrounding it. The content of the pilot analysis is a simplified decision model, a tentative preference structure, and a rough characterization of uncertainty. From a pilot analysis, the decision-maker should expect preliminary recommendations for the decision and the analyst should expect guidance in conducting the full-scale analysis.
The purpose of the full-scale analysis is to find the most desirable action, given the fully developed decision basis. The full-scale analysis consists of a balanced and realistic decision model, preferences that have been certified by the decision-maker, and a careful representation of important uncertainties. From the full-scale analysis, the decision-maker should expect a recommended course of action.
While most analyses progress from pilot to full-scale, some are so complex that valuable distinctions may be made between different stages of full-scale analysis.
The first stage of full-scale analysis is the prototypical stage, which is intended to reveal weaknesses and excesses in the full-scale analysis that are worthy of correction. A prototype is defined as "an original type, form, or instance that serves as a model on which later stages are based or judged."
After the indicated corrections have been made, the analyst has an integrated stage of full-scale analysis that provides the decision-maker with confidence in having a unified, balanced, and economic analysis as a basis for decision. To integrate is "to make into a whole by bringing all parts together: unify." If a decision-maker is making a personal decision that will not require the support or approval of others, then the integrated stage of full-scale analysis is all that is required. However, if the decision-maker must convince others of the wisdom of the chosen course of action or even defend that course against hostile elements, then an additional stage of full-scale analysis will be necessary — the defensible stage.
The defensible stage of full-scale analysis is intended to demonstrate to supportive, doubtful, and possibly hostile audiences that the analysis provides an appropriate basis for decision. Defensible means "capable of being defended, protected, or justified." Typically, defensible analyses are necessary for important decisions in the public arena; however, even private enterprises may wish to conduct defensible analyses to win the support of workers, financial institutions, or venture partners. Defensible analyses are very demanding because they must show not only that the basis used is reasonable, but also that other possible bases that would lead to different decisions are not reasonable.
Contributions from Psychological Research
One of the most significant factors influencing the practice of decision analysis in recent years has been new knowledge about cognitive processes from the field of psychology. This research, centering on the contribution of Kahneman and Tversky, has had two major effects. First, the research on cognitive biases [10] has shown the need for subtlety and careful procedure in eliciting the probabilistic judgments on which decision analysis depends. Second, and perhaps even more important the descriptive research on how people actually make decisions [6,11] shows that man is considerably less skilled in decision-making than expected. The main thrust of this research shows that people violate the rules of probabilistic logic in even quite simple settings. When we say that people violate certain rules, we mean that when they are made aware of the implications of their choices, they often wish they had made another choice: that is, they realize they have made a mistake. While these mistakes can be produced in analyzing simple decision settings, they become almost unavoidable when the problem is complex.
These findings may change our interpretation of the logical axioms that are the foundations of decision analysis. We have always considered these axioms as normative: they must be satisfied if our decisions are to have many properties that we would regard as desirable. If a particular individual did not satisfy the axioms, then he would be simply making mistakes in the view of those who followed the axioms. While this interpretation is still possible, a more appropriate way to look at the axioms is that they describe what any person would do if faced with a situation as simple as the one described by the axioms. In other words, the axioms are descriptive of human behavior for simple situations. If, however, the situation becomes more complex, more "opaque" as opposed to "transparent," the axioms are no longer descriptive because the person may unintentionally violate the axiom systems.
We may now think of the job of the decision analyst as that of making "opaque" situations "transparent," so that the person clearly sees what t do. This interpretation of the work may not make it any easier, but it is far more humane than the view that the analyst is trying to impose logic willfully illogical world.
Influence Diagrams
The influence diagram is one of the most useful concepts developed in decision analysis [3]. The analyst has always faced the problem of how to reduce the multifaceted knowledge in people's heads to a form that could meet the rigid tests of explicitness and consistency required by a computer. The analyst has always faced the problem of how to reduce the multifaceted knowledge in people's heads to a form that could meet the rigid tests of explicitness and consistency required by a computer. The influence diagram is a major aid in this transformation because it cross the border between the graphic view of relationships that is very convenient for human beings and the explicit equations and numbers that are the province of present computers. To find a device that can readily be sketched by a layman and yet be so carefully defined that useful theorems concerning it can be proved by formal methods is rare. Although there is a danger that people who do not thoroughly understand influence diagrams may abuse them and be misled, there is an even greater promise that the influence diagram will be an important bridge between analyst and decision-maker.
Valuing Extreme Outcomes
One of the problems perplexing early users of decision analysis was how to treat outcomes so extreme that they seemed to be beyond analysis. For example, the question of how a person's death as the result of medical treatment can be balanced with other medical outcomes, like paralysis or even purely economic outcomes, was especially demanding. These problems appear to raise both ethical dilemmas and technical difficulties. One ethical dilemma centered on who had the right to value lives. A technical difficulty was revealed when an economist testifying in court on the value of a life was asked whether he would be willing to allow himself to be killed if he were given that amount of money. Nevertheless, once the ethical issue is clarified by acknowledging that a person may properly place a value on his own life, then the technical question of how to do it can be addressed quite satisfactorily, especially in the case of exposure to the many small risks present in modern life [4,5]. The results have major implications for many decisions affecting health and safety.
The development of ways to think about the unthinkable has shown that no decision problem lies beyond the realm of decision analysis. That is very satisfying, for were you faced with medical decisions about a loved one, would you want to use second-rate logic any more than a second-rate doctor?
Conclusion
When decision analysis was first developed, a common comment was, "If this is such a great idea, why doesn't [insert name of large, famous company] use it?" Today, it is difficult to find a major corporation that has not employed decision analysis in some form. There are some factors that should lead to even greater use. For example, decision analysis procedures are now more efficiently executable because the increased power of modern computers has reduced the costs of even very complex analyses to an affordable level. The problems that can be successfully attacked now run the gamut of all important decision problems. Increasing uncertainties and rapid change require fresh solutions rather than tested "rules of thumb." Some day, decision analysis of important decisions will perhaps become recognized as so necessary for conducting a provident life that it will be taught in grade school rather than in graduate school.
References:
1. Ronald A Howard, "Decision Analysis: Applied Decision Theory," Proceedings of the Fourth International Conference on Operational Research, Wiley-Interscience, New York, 1966, pp. 55-71.
2. Ronald A Howard, "The Foundations of Decision Analysis," IEEE Transactions on Systems Science and Cybernetics, SSC-4, No. 3, (September 1968): 211-19.
3. Ronald A Howard and James E Matheson, "Influence Diagrams," Department of Engineering-Economic Systems, Stanford University, July 1979.
4. Ronald A Howard, "On Making Life and Death Decisions," Societal Risk Assessment, How Safe Is Safe Enough?, Edited by Richard C. Schwing and Walter A Albers, Jr, General Motors Research Laboratories, Plenum Press, New York, 1980.
5. Ronald A Howard, James E Matheson, and Daniel L Owen, "The Value of Life and Nuclear Design," Proceedings of the American Nuclear Society Topical Meeting on Probabilistic Analysis of Nuclear Reactor Safety, American Nuclear Society, May 8-10, 1978.
6. Daniel Kahneman and Amos Tversky, "Prospect Theory: An Analysis of Decision under Risk," Econometrica, 47, No. 2 (March 1979): 263-291.
7. D Warner North, "A Tutorial Introduction to Decision Theory," IEEE Transactions on Systems Science and Cybernetics, SSC-4, No 3, (September 1968): 200-10.
8. Howard Raiffa, Decision Analysis: Introductory Lectures on Choices under Uncertainty, Addison-Wesley, 1968.
9. Myron Tribus, Rational Descriptions, Decisions, and Designs, Pergamon Press, 1969.
10. Amos Tversky and Daniel Kahneman, "Judgment under Uncertainty: Heuristics and Biases," Science, 185 (Sept 27, 1974): 1124-1131.
11. Amos Tversky and Daniel Kahneman, "The Framing of Decisions and the Psychology of Choice," Science, 211 (Jan 30, 1981): 453-458.
Republished with permission of The Stanford Decisions and Ethics Center
We the People Need to Know What is Happening and Why…
With the US investigation of what happened at BP and its failed deep-water oil rig only beginning, the public should not be shy in its demands for full disclosure of the evidence and findings. The BP oil spill has placed society on notice that egregious engineering and risk management decisions are placing our nation and world increasingly at risk. I therefore echo the call by Dr Nansen G Saleri of Quantum Reservoir Impact for a thorough public investigation into the BP oil spill disaster:
Source: Saleri, N G (2010, May 7), Learn From the BP Disaster, Then Drill Again, Wall Street Journal Online.
What is needed is a scientific, thorough and apolitical investigation headed possibly by the National Academy of Sciences and drawing in experts from the oil and gas industry as well as the government agencies involved. The investigation must also evaluate the entire post-accident response effort led by BP in cooperation with local, state and federal agencies.The ecological and environmental catastrophe now underway in the Gulf of Mexico, coupled with the ongoing monetary crisis that has gripped global markets, are clear evidence that the decisions of governments and multi-national corporations are placing society and our world increasingly at risk. We the people need to know what is happening and why…
Some questions that must be diligently probed by investigators are: 1) Why did the blowout preventers—the massive valve assemblies designed to stop an uncontrolled flow—fail? And what are their reliability statistics? 2) Were the redundant safety systems truly redundant? It seems obvious they weren't, but this has to be verified. 3) How well trained was the crew? 4) Were the safety systems and contingency plans in place commensurate with the immense values of the total assets at risk—human, material and environmental? 5) Did operational and cost-cutting practices compromise safety?
Source: Saleri, N G (2010, May 7), Learn From the BP Disaster, Then Drill Again, Wall Street Journal Online.
Saturday, June 05, 2010
Enterprise Risk Management is a Farce
In the wake of the still expanding ecological crisis in the Gulf of Mexico, management experts are beginning to ask critical questions about the nature and effectiveness of enterprise risk management. Sam Friedman of National Underwriter Property and Casualty reaches this conclusion:
The BP oil spill is not only a stain upon enterprise risk management as an occupation, but upon the entire US regulatory establishment as well. Let’s face it, enterprise risk management in America is a farce.
Source: Friedman, S (2010, May 31), BP Oil Spill a Stain on Risk Management, National Underwriter Property & Casualty.
The BP oil spill disaster exposed serious shortcomings in both technology and regulation, but the biggest culprit is a catastrophic failure of enterprise risk management.Most Americans presume that a multi-billion dollar energy firm of BP’s stature has the resources, technology, and skills to recover oil in a manner that is both profitable and safe for society. However, the extent of the ongoing catastrophe defies this presumption. According to Friedman:
It's hard to imagine a scenario any worse than this. An offshore oil rig explodes, killing 11 workers. The rig collapses. Oil keeps gushing from a deep-sea well, threatening the Gulf Coast, Florida Keys and perhaps even the Eastern Seaboard.Of course, even multi-billion dollar global corporations can make mistakes, and so society takes great comfort in knowing that the Federal government is diligently regulating and inspecting high-risk industries in order to protect its citizens from the deleterious effects of ecological devastation. Once again, the public apparently presumes too much.
The BP oil spill is not only a stain upon enterprise risk management as an occupation, but upon the entire US regulatory establishment as well. Let’s face it, enterprise risk management in America is a farce.
Source: Friedman, S (2010, May 31), BP Oil Spill a Stain on Risk Management, National Underwriter Property & Casualty.
Information Technology Is Not Smart, People Are…
Dr Andrew McAfee (2010) recently made the following observations about computers in enterprise:
My personal observation is that firms today tend to throw money at IT and then rush into the claim that they are therefore more analytic, collaborative, and agile as a result -- the reality is very different on the ground. Decision support and information technology are critical resources for the conduct of enterprise, however these functions and installations remain a supporting effort to the larger tasks of predicting and optimizing. The weak link in the business intelligence production chain is not IT, but smarts...
Source: McAfee, A (2010, June 3), IT's Three Key Organizational Transformations, Harvard Business Review Blog.
I see companies in all industries using computers to accomplish three broad and deep transformations: they're becoming more scientific, more orchestrated, and more self-organizing.I sincerely hope that large-scale enterprises might someday become more scientific, orchestrated, and self-organizing, and I agree that these are the hopes and promises of information technology (IT). However, the case is still out as to whether IT can or will deliver on these promises by itself.
My personal observation is that firms today tend to throw money at IT and then rush into the claim that they are therefore more analytic, collaborative, and agile as a result -- the reality is very different on the ground. Decision support and information technology are critical resources for the conduct of enterprise, however these functions and installations remain a supporting effort to the larger tasks of predicting and optimizing. The weak link in the business intelligence production chain is not IT, but smarts...
Source: McAfee, A (2010, June 3), IT's Three Key Organizational Transformations, Harvard Business Review Blog.
On Immigration Reform
Anyone who believes that closing and securing our 2,000 mile border with Mexico is a trivial task is delusional. Vast resources, including many thousands of guards or even infantry would be required to "seal" the border. Moreover, the very idea of militarizing the US-Mexican border or deporting long-term residents is insane. Either our Congress does the hard work of reforming our immigration laws, or America lives with the status quo. The time has arrived for Congress to pass comprehensive immigration reform for the good of the nation...
Friday, June 04, 2010
Lingering Job Losses Worst Since World War II
A comparison of percent job losses during the current recession with past recessions indicates that the US is experiencing the deepest drop in national employment since World War II. In other words, most Americans alive today have not experienced a deeper decline in national employment during their lifetimes.
The chart below tells the story (click to expand).
Note that the red dotted line depicts the percent job losses through May, less the 411,000 temporary census jobs the government recently added (and which will end in July).
Source: McBride, B (2010, June 4), May Employment Report: 20K Job ex-Census, 9.7% Unemployment Rate, Calculated Risk.
The chart below tells the story (click to expand).
Note that the red dotted line depicts the percent job losses through May, less the 411,000 temporary census jobs the government recently added (and which will end in July).
Source: McBride, B (2010, June 4), May Employment Report: 20K Job ex-Census, 9.7% Unemployment Rate, Calculated Risk.
The Probability of Sequential Catastrophic Disasters Hitting the US
Let's hypothetically assume that:
Of course, the assumptions above are hypothetical. Nonetheless, the analysis leads me to suspect that the levels of risk assumed by the financial services and energy sectors in recent years have evidently been quite high, and certainly much higher than those portrayed above. Or perhaps recent events in the US are simply one of those one in one hundred million anomalies of fate...
Related Posts:
The Financial Economics of Synthetic Catastrophe
- The probability of a catastrophic financial meltdown occurring during a given 5-year period is one in ten thousand (1:10,000); and
- The probability of a catastrophic environmental disaster occurring during a given 5-year period is also one in ten thousand (1:10,000).
Of course, the assumptions above are hypothetical. Nonetheless, the analysis leads me to suspect that the levels of risk assumed by the financial services and energy sectors in recent years have evidently been quite high, and certainly much higher than those portrayed above. Or perhaps recent events in the US are simply one of those one in one hundred million anomalies of fate...
Related Posts:
The Financial Economics of Synthetic Catastrophe
Tuesday, June 01, 2010
The Financial Economics of Synthetic Catastrophe
The BP deep water oil catastrophe in the Gulf of Mexico is beginning to generate lessons from economists. Prof Kenneth Rogoff (2010) offers this early conclusion regarding the evolution and emergence of risk economics in an increasingly complex world:
Source: Ragoff, K (2010, June 1), The BP Oil Spill’s Lessons for Regulation, Project Syndicate.
Related Posts:
Risk versus Uncertainty
Economics teaches us that when there is huge uncertainty about catastrophic risks, it is dangerous to rely too much on the price mechanism to get incentives right. Unfortunately, economists know much less about how to adapt regulation over time to complex systems with constantly evolving risks, much less how to design regulatory resilient institutions. Until these problems are better understood, we may be doomed to a world of regulation that perpetually overshoots or undershoots its goals.The regulation of risk is bound to expand in the coming years. However, the time has also come for society to improve its understanding of uncertainty and risk in the post-modern age. The financial economics of synthetic catastrophe are central to the future of capitalism in the new millenium.
Source: Ragoff, K (2010, June 1), The BP Oil Spill’s Lessons for Regulation, Project Syndicate.
Related Posts:
Risk versus Uncertainty
Imagine a Garden...
Imagine there's no heaven
It's easy if you try
No hell below us
Above us only sky
Imagine all the people
Living for today...
Imagine there's no countries
It isn't hard to do
Nothing to kill or die for
And no religion too
Imagine all the people
Living life in peace...
You may say I'm a dreamer
But I'm not the only one
I hope someday you'll join us
And the world will be as one
Imagine no possessions
I wonder if you can
No need for greed or hunger
A brotherhood of man
Imagine all the people
Sharing all the world...
You may say I'm a dreamer
But I'm not the only one
I hope someday you'll join us
And the world will live as one
~ John Lennon (1971)
Related Posts:
The Vantage Point
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