According to leading economic scholars, economic agents are optimizers. Rational behavior is maximization in the pursuit of self interest. Firms try to maximize profits, households try to maximize utility, governments try to maximize social welfare. Those who fall short of the optimum are weeded out by competition. Optimization has its own moral imperative: we should all do our best, and if we don't, then it's the other guy's turn at bat.
Is this true? Should we rely on economic models which adopt maximization as an axiom?
Biological evolution is a powerful metaphor for economics. Consider a squirrel nibbling acorns, and noticing a stand of fine oaks in the distance. There are probably better acorns there, but also other squirrels and predators. How long should the squirrel forage here before moving there? What strategy should guide the decision? The squirrel needs a critical amount of energy to survive the night. Maximizing caloric intake is not necessary. Maximizing the reliability of achieving the critical intake is necessary. What is maximized is not the substantive "good" (calories), but confidence in satisfying a critical requirement.
Fifty years ago, Herbert Simon (Nobel Prize in economics, 1978) advanced the idea that economic agents lack information, understanding, and the ability to process data. These deficiencies, which he called their "bounded rationality", force agents to look for solutions which are good enough, though not necessarily optimal. The optimum may exist but it cannot be known by the resource- and information-limited agent. "Satisficing" is what Simon called this strategy of settling for a solution which is good enough, as opposed to optimizing.
But academic economists seem to take scarce notice of Simon's work. Like Twain's quip about the weather, they all talk about it (either weather or satisficing) but they don't do a damn thing. Rationality, we learn, is optimization of profit or utility.
This very conventional attitude to rationality may be related to the long list of unresolved economic paradoxes. One example began with a 1985 article by Mehra and Prescott (the latter won the 2004 Nobel Prize in economics) entitled "The equity premium: A puzzle". A decade later Kocherlakota published "The equity premium: It's still a puzzle". There are many theoretical explanations of the equity premium puzzle (EPP), but no consensus. In fact, there is no consensus that a long-term equity premium even exists.
What is the EPP, and what can we learn about optimization in economics?
Stocks are riskier than US government bonds, so the average return to stocks should be higher. Otherwise who would look at stocks? This is sound common sense, and many economists have shown that the annual return to stocks is higher than to bonds, typically by 7%, sometimes by as much as 20% or as little as 0.3%. Does this "risk premium" for stocks make sense in terms of rational (read: maximizing) behavior? The puzzle (assuming the premium is real) is that standard asset pricing models can explain the EPP only by assuming that investors are much too averse to risk. The observed behavior of investors in other risky settings would suggest that they would be willing to accept a much lower equity premium for stocks.
There are, as noted, many attempts to resolve the EPP, including that it is a statistical chimera. But what these explanations have usually not challenged is the assumption of optimization. Explanations of the EPP usually assume that investors try to maximize their returns rather than trying to achieve adequately large returns (e.g., larger than the competition).
Robustness to uncertainty is the key to understanding the role of satisficing in explaining the EPP.
Suppose we hear that so-and-so offers higher returns than anyone else, though there are many risks involved. While such large returns would be great, the investor would be satisfied with lower returns. Lower gains are offered by more than one alternative option. This means that by relinquishing the goal of maximizing (even on average), the investor is able to choose among more alternatives, all of which are equivalent in terms of expected average return. The investor who satisfices (rather than maximizes) can choose the alternative which would yield the required return over the greatest range of uncertain future scenarios. That is, the investor foregoes some aspiration for profit in exchange for some immunity against surprise. In other words, satisficing is more robust to uncertainty than optimizing. If satisficing - rather than maximizing - is in some sense a better bet, then it will tend to persist under uncertain competition.
Now we can understand that equity premia should be lower than predicted by theories which assume that investors try to maximize their returns. Satisficers have to satisfy their requirements (or those of their clients). This may mean beating the competition over the long run, but it does not mean being the best that is conceivable. Satisficing strategies tend to persist because they robustly meet critical requirements. And since satisficing entails a preference for less-than-maximal options, we should expect the market to lower the equity premium for risk.
There is a broader policy implication of the logic of satisficing. Going after critical requirements is a better bet for "survival", than going after what seems optimal. This is true of all decisions under uncertainty: monetary and fiscal policy, resource economics, pollution control, climate change, etc. We must ask what outcomes are critical, not what are the best predicted outcomes. Critical requirements are usually more modest than the best anticipated outcome, so there will usually be numerous ways to achieve them. We should choose the option which will lead to the required outcome most robustly.
Reasoned pursuit of self-interest? Yes. Use of data, models and theories to inform our decisions? Yes. But economists who equate optimization of outcomes with economic rationality are mistaken. And regarding scholars in general, let's recall the advice of the ancient Jewish sage Akiva: ``Don't live in a town whose mayor is a scholar" (Talmud, Pesachim, ch.10, 112:a).
in my limited and scholastic knowledge of finance ( I have a MBA in finance which I have never really used in the work place except for a few exceptions), firm evaluations are flawed. DCF does not work because it is too "static". Real options models could work in some cases althought most of the time the option price is computed using the European Option which is often not a good model, decision trees are garbage:-)...Hence I want to start from a general question: we are all faced with everyday decisions in the presence of uncertainty. It is the rule of life. The problem is making decisions that we cannot change later on when "environmental conditions" change and therefore, the company we decided to invest went bust because of act of God (say an earthquake that destroyed the supply chain). Real options model try to provide this flexibility that's lacking in DCF's. What are the merits and demerits of the info-gap compared to real options model? How well does it scale for real life problems? Thanks, Paolo
Thanks for your comment. Your question has two parts: how does info-gap theory compare with real options, and is info-gap theory applicable to real problems.ReplyDelete
Let me address these questions together, by explaining very briefly the basic idea of info-gap theory.
Let's suppose that you must make a decision, based on data, understanding, models, and goals. For instance, you wish to make an investment decision based on a real options valuation (ROV). Very briefly, the ROV requires knowledge of possible future outcomes and available investor reactions, among other things. If the investor is confident that this information is available then the analysis proceeds confidently. However, there may be a disparity between what the investor needs to know (such as the plausible characterization of future contingencies) and what is actually known. This disparity is an info-gap, and it limits the the confidence in the ROV analysis.
Info-gap theory is a methodology for dealing with info-gaps, such as described in the previous paragraph. Info-gap theory doesn't invent knowledge, but it does help manage its absence. The analyst has an understanding of the future, (e.g. of what contingencies could arise, etc) but is uncertain how wrong that understanding will turn out to be. An info-gap analysis begins by non-probabilistically quantifying this uncertainty. Then we ask: how wrong could our understanding be, and the investment we are considering will lead to an acceptable outcome? The answer to this question is the info-gap robustness. More robustness is better than less robustness, so the robustness establishes preferences over investments (or investment strategies).
In summary, the question "How does info-gap compare with real options?" is not really the right question. The questions that need to be asked are "Do we confidently know enough to implement the ROV?" If the answer is affirmative, then do the ROV analysis and forget about info-gap theory. If the answer is negative, then we ask "How can info-gap supplement real options (or any other decision methodology) by modeling and managing the uncertainties?"
This is only the very briefest of summaries. There is much more to info-gap theory, (for instance, the opportuneness function) just as there is much more to real options valuation. You may wish to read more about info-gap theory in general or about info-gap applied to economics in particular. Take a look at http://info-gap.com for lots of source material.
Is info-gap theory useful in real decisions? Not always. It depends on the decision and its context. However, there are many examples where info-gap provides clear added value in decision making. Take a look at the many publications (many by authors other than myself) which are listed on info-gap.com.
I would feel very disappointed if ethics turned out to be no more than well understood self interest.ReplyDelete
Economic downturns such as (supposedly) in 2008 and now, are now commonly explained as utility maximization by greedy bankers and politicians.
I believe that groupsism and cronyism are the culprits, only counting social reality between people within groups, not within people between groups because that is less salient.