Any event in the history of the organism is, in a sense, unique. Consequently, recognition, learning, and judgment presuppose an ability to categorize stimuli and classify situations by similarity As Quine (1969) puts it: There is nothing more basic to thought and language than our sense of similarity ; our sorting of things into kinds [p 1161 . Indeed, the notion of similarity that appears under such different names as proximity, resemblance, communality, representativeness, and psychological distance is fundamental to theories of perception, learning, and judgment This chapter outlines a new theoretical analysis of similarity and investigates some of its empirical consequences The theoretical analysis of similarity relations has been dominated by geometric models. Such models represent each object as a point in some coordinate space so that the metric distances between the points reflect the observed similarities between the respective objects In general, the space is assumed to be Euclidean, and the purpose of the analysis is to embed the objects in a space of minimum dimensionality on the basis of the observed similarities, see Shepard (1974) In a recent paper (Tversky, 1977), the first author challenged the dimensionalmetric assumptions that underlie the geometric approach to similarity and developed an alternative feature-theoretical approach to the analysis of similarity relations. In this approach, each object a is characterized by a set of features, denoted A, and the observed similarity of a to b, denoted s(a, b), is expressed as a function of their common and distinctive features (see Fig 4.1) That is, the observed similarity s(a, b) is expressed as a function of three arguments : A f1B, the features shared by a and b ;A B, the features of a that are not shared by b ; B A, the features of b that are not shared by a Thus the similarity between

The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important social, medical, and political situations as well. Individual chapters discuss the representativeness and availability heuristics, problems in judging covariation and control, overconfidence, multistage inference, social perception, medical diagnosis, risk perception, and methods for correcting and improving judgments under uncertainty. About half of the chapters are edited versions of classic articles; the remaining chapters are newly written for this book. Most review multiple studies or entire subareas of research and application rather than describing single experimental studies. This book will be useful to a wide range of students and researchers, as well as to decision makers seeking to gain insight into their judgments and to improve them.

The APA Awards for Distinguished Scientific Contributions are presented to persons who, in the opinion of the Committee on Scientific Awards, have made distinguished theoretical or empirical contributions to basic research in psychology. The 2015 recipients of the APA Scientific Contribution Awards were recognized by the 2014 Board of Scientific Affairs and selected by the 2014 Committee on Scientific Awards. The winners for 1956 through 2015 are listed here. The 2015 award winners are Stanislas Dehaene, Edna B. Foa, and Michael Tomasello. (PsycINFO Database Record

Perhaps the simplest and the most basic qualitative law of probability is the conjunction rule: The probability of a conjunction, P (A&B) cannot exceed the probabilities of its constituents, P (A) and P (B), because the extension (or the possibility set) of the conjunction is included in the extension of its constituents. Judgments under uncertainty, however, are often mediated by intuitive heuristics that are not bound by the conjunction rule. A conjunction can be more representative than one of its constituents, and instances of a specific category can be easier to imagine or to retrieve than instances of a more inclusive category. The representativeness and availability heuristics therefore can make a conjunction appear more probable than one of its constituents. This phenomenon is demonstrated in a variety of contexts including estimation of word frequency, personality judgment, medical prognosis, decision under risk, suspicion of criminal acts, and political forecasting. Systematic violations of the conjunction rule are observed in judgments of lay people and of experts in both between-subjects and within-subjects comparisons. Alternative interpretations of the conjunction fallacy are discussed and attempts to combat it are explored.

Theories of subjective probability are viewed as formal languages for analyzing evidence and expressing degrees of belief. This article focuses on two probability langauges, the Bayesian language and the language of belief functions (Shafer, 1976). We describe and compare the semantics (i.e., the meaning of the scale) and the syntax (i.e., the formal calculus) of these languages. We also investigate some of the designs for probability judgment afforded by the two languages.

This paper considers the role of reasons and arguments in the making of decisions. It is proposed that, when faced with the need to choose, decision makers often seek and construct reasons in order to resolve the conflict and justify their choice, to themselves and to others. Experiments that explore and manipulate the role of reasons are reviewed, and other decision studies are interpreted from this perspective. The role of reasons in decision making is considered as it relates to uncertainty, conflict, context effects, and normative decision rules.

This essay discusses the field of behavioral economics, with a focus on the papers in Advances in Behavioral Economics. These papers show that there is a body of “behavioral facts” that is both economically significant and regular enough to be modeled. For the field to advance further, it should devote more attention to the foundations of its models, and develop unified explanations for a wider range of phenomena.

Axiomatic theories of choice introduce preference as a primitive relation, which is interpreted through specific empirical procedures such as choice or pricing. Models of rational choice assume a principle of procedure invariance, which requires strategically equivalent methods of elicitation to yield the same preference order. Thus, if the decision maker prefers A to B, then the cash equivalent, or minimum selling price, of A should exceed that of B. However, there is a substantial body of evidence showing that the price ordering of risky prospects is systematically different from the choice ordering, contrary to standard theories of choice.

Trees are commonly used to represent proximity relations that emerge, for instance, from studies of classification, similarity, and identification. Trees are employed to describe the data, explore their structure and model their generating process. They offer a convenient graphical display that is readily interpretable in terms of a hierarchy of clusters (Sokal &Sneath, 1963) or in terms of common and distinctive features (Tversky, 1977). The simplest tree structure is the hierarchical clustering model (Jardine & Sibson, 1971; Johnson, 1967) based on the ultrametric inequality, which states that for any triple of points the two larger distances are equal. That is, any three points can be labeled x, y, z such that d(x, z) = d(y, z) >_ d(x, y). This assumption gives rise to a tree in which all the endpoints (leaves) are equally distant from the root. The ultrametric tree is highly restrictive because any two elements of one cluster must be equally similar to any other element outside the cluster. This restriction is relaxed in the additive tree (e.g., Cunningham, 1978; Sattath & Tversky, 1977), where the leaves are not necessarily equidistant from the root. The additive tree provides greater flexibility than the ultrametric tree, but it too cannot accomodate (nonnested) overlapping clusters because any two clusters in a tree are either nested or disjoint. Throughout the paper we use the standard abbreviations (e.g., HICLUS, ADDTREE, ADCLUS) for scaling algorithms, and the unabbreviated forms (e.g., hierarchical clustering, additive tree, additive clustering) for the respective models. This article describes a new representation of proximity relations, called an extended tree, which accommodates nonnested feature structures while maintaining the basic property of a tree that every pair of points is joined by a unique path. To motivate and

Amos Tversky (1937-1996), a towering figure in cognitive and mathematical psychology, devoted his professional life to the study of similarity, judgment, and decision making. He had a unique ability to master the technicalities of normative ideals and then to intuit and demonstrate experimentally their systematic violation due to the vagaries and consequences of human information processing. He created new areas of study and helped transform disciplines as varied as economics, law, medicine, political science, philosophy, and statistics. This book collects forty of Tversky's articles, selected by him in collaboration with the editor during the last months of Tversky's life. It is divided into three sections: Similarity, Judgment, and Preferences. The Preferences section is subdivided into Probabilistic Models of Choice, Choice under Risk and Uncertainty, and Contingent Preferences. Included are several articles written with his frequent collaborator, Nobel Prize-winning economist Daniel Kahneman.

This paper explores a judgmental heuristic in which a person evaluates the frequency of classes or the probability of events by availability, i.e., by the ease with which relevant instances come to mind. In general, availability is correlated with ecological frequency, but it is also affected by other factors. Consequently, the reliance on the availability heuristic leads to systematic biases. Such biases are demonstrated in the judged frequency of classes of words, of combinatorial outcomes, and of repeated events. The phenomenon of illusory correlation is explained as an availability bias. The effects of the availability of incidents and scenarios on subjective probability are discussed.

We discuss the cognitive and the psy- chophysical determinants of choice in risky and risk- less contexts. The psychophysics of value induce risk aversion in the domain of gains and risk seeking in the domain of losses. The psychophysics of chance induce overweighting of sure things and of improbable events, relative to events of moderate probability. De- cision problems can be described or framed in multiple ways that give rise to different preferences, contrary to the invariance criterion of rational choice. The pro- cess of mental accounting, in which people organize the outcomes of transactions, explains some anomalies of consumer behavior. In particular, the acceptability of an option can depend on whether a negative outcome is evaluated as a cost or as an uncompensated loss. The relation between decision values and experience values is discussed. Making decisions is like speaking prose—people do it all the time, knowingly or unknowingly. It is hardly surprising, then, that the topic of decision making is shared by many disciplines, from mathematics and statistics, through economics and political science, to sociology and psychology. The study of decisions ad- dresses both normative and descriptive questions. The normative analysis is concerned with the nature of rationality and the logic of decision making. The de- scriptive analysis, in contrast, is concerned with peo- ple's beliefs and preferences as they are, not as they should be. The tension between normative and de- scriptive considerations characterizes much of the study of judgment and choice. Analyses of decision making commonly distin- guish risky and riskless choices. The paradigmatic example of decision under risk is the acceptability of a gamble that yields monetary outcomes with specified probabilities. A typical riskless decision concerns the acceptability of a transaction in which a good or a service is exchanged for money or labor. In the first part of this article we present an analysis of the cog- nitive and psychophysical factors that determine the value of risky prospects. In the second part we extend this analysis to transactions and trades. Risky Choice Risky choices, such as whether or not to take an umbrella and whether or not to go to war, are made without advance knowledge of their consequences. Because the consequences of such actions depend on uncertain events such as the weather or the opponent's resolve, the choice of an act may be construed as the acceptance of a gamble that can yield various out- comes with different probabilities. It is therefore nat- ural that the study of decision making under risk has focused on choices between simple gambles with monetary outcomes and specified probabilities, in the hope that these simple problems will reveal basic at- titudes toward risk and value. We shall sketch an approach to risky choice that