October 31, 2007

Summary

Judgment under Uncertainty: Heuristics and Biases
Amos Tversky and Daniel Kahneman
Tversky and Kahneman use this article to summarize and explain a compilation of heuristics and biases that hinder our ability to judge probabilities of uncertain events. The article is categorized into discussions of 3 main heuristics and examples of biases each heuristic leads to. I will summarize these in outline form for ease of organization.
• Representativeness heuristic. When using the representativeness heuristic, people make judgments about probability based on how well it represents, or is similar to a stereotype they are familiar with. The closer it resembles the stereotype, the higher they consider the probability to be that it fits the stereotype. This heuristic is usually used when one is asked to judge the probability that an object or event belongs to a specific class or process.
o Insensitivity to prior probability of outcomes. This is a phenomenon where people ignore prior probabilities when they evaluate probability by representativeness. However, people do use prior probabilities correctly when they have no other information to go on.
o Insensitivity to sample size. People assume that characteristics of a population will hold no matter what the sample size is, whereas this is not a safe assumption in small sample sizes.
o Misconceptions of chance. One consequence of this is the gambler’s fallacy, where chance is viewed as a self-correcting process, which is not true in a series of independent events.
o Insensitivity to predictability. This describes the bias in which people feel comfortable making intuitive predictions based on insufficient information.
o Illusion of validity. The confidence a person has in their ability to predict something is based primarily on its degree of representativeness of what it is being compared to without considering factors that may limit predictability.
o Misconceptions of regression. People look at individual instances of performance independently, without considering the effects of regression toward the mean.
• Availability heuristic. People sometimes judge the frequency of an event based on instances that can be brought to mind at the time. This heuristic is often used when one is asked to assess the frequency of an event or the plausibility of a development.
o Biases due to the retrievability of instances. If one group has more instances that are more familiar or more salient than another group, the first group will seem to be bigger, even if the two groups are the same size.
o Biases due to the effectiveness of a search set. When people are asked to solve a problem that requires them to elicit a search set, they will decide on the answer to the problem based on ease of search due to information that is available, rather than the effectiveness of the search.
o Biases of imaginability. When trying to judge the frequency of an event in which instances need to be imagined to try to decide on the frequency, the frequency will be based on how easy it is to imagine various instances of the event.
o Illusory correlation. If two events are strongly associated, they are judged to occur together more frequently.
• Adjustment and Anchoring Heuristic. People many times make estimates by adjusting from an initial value (anchoring point) to obtain a final value. This heuristic is used in numerical prediction when a relevant value is available.
o Insufficient adjustment. When starting from an initial value and adjusting, the adjustment is often much smaller than what it should be.
o Biases in the evaluation of conjunctive and disjunctive events. Because of anchoring, probability is often overestimated in conjunctive problems and underestimated in disjunctive problems.
o Anchoring in the assessment of subjective probability distributions. Because of anchoring, when people are forming subjective probability distributions, many times the distributions are too tight in relation to the actual probability distributions.
In the discussion, the authors point out that these heuristics and biases are not only experienced by naïve people, but also by researchers who are aware of this theory. The lack of an appropriate code is a reason that people don’t detect their own biases when they make decisions. Also, internal consistency is not enough to consider judged probabilities to be accurate.

Analysis of Heuristics and Biases paper according to Meister’s characteristics:
Primary Topic. The topic is Heuristics and Biases.
Specific Theme. The theme is that certain heuristics in decision making lead to various biases.
Venue. Various venues were used for research, mostly universities.
Subjects. Various subjects used, including undergraduate students and researchers.
Methodology. Various studies performed contributed to this work, but many of the studies involved asking subjects to solve problems.
Theory Involvement. The theory discussed in this paper evolved from previous studies, which are used to explain and validate the theory.
Research Type. This article is kind of a theoretical discussion of a compilation of previous research.
Unit of Analysis. The unit of analysis in this paper is the individual person making decisions.
Hypothesis. Biases in judgments reveal some heuristics of thinking under uncertainty.
Design Application. Heuristics and biases are important to consider in the design of any system where human decision making will occur.

Prospect Theory: An Analysis of Decision under Risk
Daniel Kahneman and Amos Tversky
Kahneman and Tversky begin this paper by giving a critique of expected utility theory. They state that expected utility theory is based on the tenets of expectation, asset integration, and risk aversion. They then make a point to concede that their methods raise questions of validity and generalizability, but they point out that all other methods used to test utility theory raise the same concerns. The discussion then turns to observed phenomena that refute expected utility theory. With each phenomenon, examples are given of studies where hypothetical problems were given to subjects. The results of these studies validate the phenomena used to refute the tenets of the expected utility theory. The first phenomenon is the certainty effect, in which people overweight outcomes that are considered certain relative to outcomes that are only probable. The next is the reflection effect, in which the reflection of prospects around zero reverses the preference order of the prospects. The reflection effect implies that risk aversion in the positive domain is accompanied by risk seeking in the negative domain. It also eliminates aversion for uncertainty or variability as an explanation of the certainty effect. Kahneman and Tversky then state that many believe that the purchasing of insurance against both large and small losses gives evidence for the concavity of the utility theory for money, but they test this with the notion of probabilistic insurance. The results of studies involving probabilistic insurance indicate that the intuitive notion of risk is not adequately captured by the assumed concavity of the utility function for wealth. The isolation effect is the phenomenon in which people disregard components that alternatives share and focus on components that distinguish them, to simplify the choice. Instances of this effect violate a basic concept of utility theory, that choices between prospects are determined completely by the probabilities of final states.
After giving examples that clearly violate the basic principles of expected utility theory, Kahneman and Tversky explain the theory behind their descriptive model of decision making under risk, called prospect theory. Prospect theory differentiates two phases in the process of making a choice: editing followed by evaluation.
The editing phase consists of a preliminary analysis of the prospects to reorganize them into a simpler form for evaluation. It is composed of various operations including coding, combination, segregation, cancellation, simplifications, and detection of dominance. When people are presented with multiple alternatives to choose from, they first code the alternatives into gains and losses relative to a reference point. They also combine probabilities associated with identical outcomes, segregate riskless components from risky components, and discard components that are shared by all of the alternatives. Additionally, people simplify prospects by rounding probabilities or outcomes and disregard alternatives that are clearly dominated.
The next phase of decision making is the evaluation of the edited prospects. It is assumed that the evaluation will conclude in choosing the outcome with the highest value. Two basic equations are given to describe the relation between the two scales π and v to determine the overall value V. The scale, π, associates with each probability, p, a decision weight, π(p), which reflects the impact of p on V, while the scale, v, assigns a number v(x) to every outcome, x, which reflects the subjective value of that outcome. The basic equation of the theory is given as
V(x, p; y, q) = π(p)v(x) + π(q)v(y)
Where v(0) = 0, π(0) = 0, and π(1) = 1
This equation generalizes expected utility theory by relaxing the expectation principle. The evaluation of strictly positive or strictly negative prospects is described by the equation
V(x, p; y, q) = v(y) + π(p)[v(x) - v(y)]
Where p + q = 1 and either x > y > 0 or x < y < 0
The essential feature of this equation is that a decision weight is applied to the difference in value between the alternatives, which represents the risky component, but not to the riskless component v(y).
After definitions of the various components of the theory are given, they are discussed in more detail, again with examples of studies given as validation. First, the value function is discussed. The value function is defined on deviations from a reference point (not on final states), generally concave for gains and convex for losses, and steeper for losses than for gains. Next, the weighting function is discussed. One factor that affects the weighting function is that very low probabilities are usually overweighted. The property of subcertainty states that in general that the actual decision weights of all of the probabilities usually sum to less than 1, though the probabilities obviously add to 1. Also π is hypothesized to be nonlinear because it departs from linearity near the extreme values of 0 and 1. The authors point out that this theory is applied to the simple situation where a person is presented with two alternatives and that different processes may be used to make a decision in more complicated situations.
In the discussion section, the authors first explain how prospect theory accounts for observed attitudes toward risk. It then points out that there are situations in which a person’s frame of reference shifts and decisions are made based on expectations of future states rather than the current state. In addition, a person who has not accepted their current state and still uses the reference point of a recent state will make decisions accordingly. The authors finally discuss the directions in which they see future research of this theory continuing.

Analysis of Prospect Theory paper according to Meister’s characteristics:
Primary Topic. The topic is Prospect Theory.
Specific Theme. The theme is that expected utility theory is not accurate in explaining how decisions are made under risk, so a new theory is developed called Prospect Theory.
Venue. Various venues were used for research, mostly universities.
Subjects. Various subjects used, including undergraduate students and faculty.
Methodology. Various studies performed contributed to this work, but many of the studies involved asking subjects to solve problems.
Theory Involvement. The paper begins by refuting the expected utility theory and then testing and developing Prospect Theory.
Research Type. This article is a theoretical study.
Unit of Analysis. The unit of analysis in this paper is the individual person making decisions.
Hypothesis. Prospect Theory is a better descriptive model of decision under risk than expected utility theory.
Design Application. Prospect Theory would be important to consider in the design of any system where human decision making will occur under risk.

Theories of Decision-Making in Economics and Behavioral Science
Herbert A. Simon
Simon divides this article into 7 main sections: a discussion of how much psychology economics needs, developments in the theory of utility and consumer choice, the motivation of managers, the conflict of goals and the phenomena of bargaining, work on uncertainty and the formation of expectations, recent developments in the theory of human problem-solving with their implications for economic decision-making, and conclusions.
I. How Much Psychology Does Economics Need?
Simon uses a metaphor of molasses in a container to explain how the human has been thought of in economics as a simple object with clear characteristics and goals. He then discusses how economics has been changing to incorporate the more complicated system of a human and its environment which is actually a more realistic picture. He lists specific problems with classical theory.
II. The Utility Function
Simon brings up the concepts of utility theory from von Neumann and Morgenstern, and then considers the validity of the assumption of utility theory that decisions are made based on objectively determined probabilities. Understanding decision-making then becomes increasingly complicated as the situations studied become more realistic. Tools such as linear and dynamic programming help determine optimal decisions based on expected values of outcomes. The type of experiment most often used to study decision making, a situation where a person must choose between two alternatives, is defined as a binary choice experiment.
III. The Goals of Firms
Simon gives attacks on the hypothesis that the entrepreneur strives to maximize profit. Firm’s goals are not to maximize profit, but to attain a certain level of or rate of profit, holding a certain share of the market or a certain level of sales, called satisficing. There is some empirical evidence to support this theory.
IV. Conflict of Interest
Since perfect competition is a very poor assumption in reality, difficulties in imperfect competition need to be considered. Different concepts, such as game theory, power and bargaining, and games against nature, attempt to explain how decisions are made with imperfect competition assumed.
V. The Formation of Expectations
Expectations about future states have an impact on economic decisions made in the present, and empirical research that supports this is presented. It must also be assumed that people will consider the cost of information when compiling it to make a decision.
VI. Human Cognition and Economics
The limitations of the decision-maker and the complexity of the environment in which the decision-maker is operating need to be considered in predicting a decision.

Primary Topic. The topic is decision-making in economics.
Specific Theme. The theme is that considering psychological concepts in studying economic decision-making may help to predict behavior more realistically.
Venue. N/A because this is a theoretical paper.
Subjects. N/A because this is a theoretical paper.
Methodology. N/A because this is a theoretical paper.
Theory Involvement. The theory discussed in this paper comes from a compilation of studies and concepts from both psychology and economics.
Research Type. This article is a theoretical paper.
Unit of Analysis. The unit of analysis in this paper spans from the individual person making decisions to a firm or organization making decisions.
Hypothesis. Perhaps psychological concepts should be used to analyze economic decision-making in the complex world that actually exists rather than an ideal world.
Design Application. The theory in this paper applies to any system in which economic decision making occurs.

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