Case-based Decision Theory Research Articles

An experiment on case-based decision making

We experimentally investigate the disposition of decision makers to use case-based reasoning as suggested by Hume (An enquiry concerning human understanding, 1748) and formalized by case-based decision theory (Gilboa and Schmeidler in Q J Econ 110:605–639, 1995). Our subjects face a monopoly decision problem about which they have very limited information. Information is presented in a manner which makes similarity judgements according to the feature matching model of Tversky (Psychol Rev 84:327–352, 1977) plausible. We provide subjects a “history” of cases. In the $$2\times 2$$ between-subject design, we vary whether information about the current market is given and whether immediate feedback about obtained profits is provided. The results provide support for the predictions of case-based decision theory, particularly when no immediate feedback is provided.

Reason, Intuition, and Time

We study the influence of reason and intuition on decision making over time. Facing a sequence of similar problems, agents can either decide rationally according to expected utility theory or intuitively according to case-based decision theory. Rational decisions are more precise but create higher costs, though these costs may decrease over time. We find that intuition will outperform reason in the long run if individuals are sufficiently ambitious. Moreover, intuitive decisions are prevalent in early and late stages of a learning process, whereas reason governs decisions in intermediate stages. Examples range from playing behavior in games like Chess to professional decisions during a manager’s career. JEL-Code: D810, D830, C630.

Evaluating case-based decision theory: Predicting empirical patterns of human classification learning

We introduce a computer program which calculates an agentʼs optimal behavior according to case-based decision theory (Gilboa and Schmeidler, 1995) and use it to test CBDT against a benchmark set of problems from the psychological literature on human classification learning (Shepard et al., 1961). This allows us to evaluate the efficacy of CBDT as an account of human decision-making on this set of problems.We find: (1) The choice behavior of this program (and therefore case-based decision theory) correctly predicts the empirically observed relative difficulty of problems and speed of learning in human data. (2) ‘Similarity’ (how CBDT decision makers extrapolate from memory) is decreasing in vector distance, consistent with evidence in psychology (Shepard, 1987). (3) The best-fitting parameters suggest humans aspire to an 80–85% success rate, and humans may increase their aspiration level during the experiment. (4) Average similarity is rejected in favor of additive similarity.

Experimental evidence on case-based decision theory

This paper starts out from the proposition that case-based decision theory (CBDT) is an appropriate tool to explain human decision behavior in situations of structural ignorance. Although the developers of CBDT suggest its reality adequacy, CBDT has not yet been tested empirically very often, especially not in repetitive decision situations. Therefore, our main objective is to analyse the decision behavior of subjects in a repeated-choice experiment by comparing the explanation power of CBDT to reinforcement learning and to classical decision criteria under uncertainty namely maximin, maximax, and pessimism-optimism. Our findings substantiate a predominant significantly higher validity of CBDT compared to the classical criteria and to reinforcement learning. For this reason, the experimental results confirm the suggested reality adequacy of CBDT in repetitive decision situations of structural ignorance.

Simulating Social Network Formation

Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

A Case-Based Approach to Decision AnalysisEin fallbasiertes Verfahren zur Beschlussanalyse

Abstract This paper proposes a novel approach for integrating case-based reasoning (CBR) and decision theory for casebased decision analysis. CBR is employed as a methodology to reason from previous cases for building a decision model given a current situation, while decision theory is applied to the decision model learnt from previous cases to identify the most promising, secured, and rational choices. We have also tackled the issue of imprecise utility in individual cases and explained how fuzzy decision analysis can be conducted when case specific utilities are represented with fuzzy data.

Learning and Herding Using Case-Based Decisions With Local Interactions

We evaluate repeated decisions of individuals using a variant of the case-based decision theory (CBDT), where individuals base their decisions on their own past experience and the experience of neighboring individuals. Looking at a range of scenarios to determine the successful outcome of a decision, we find that for learning to occur, agents must have a sufficient number of neighbors to learn from and access to sufficiently independent information. If these conditions are not fulfilled, we can easily observe herding in cases where no best decision exists.

An Experiment on Case-Based Decision Making

We experimentally investigate the disposition of decision makers to use case-based reasoning as suggested by Case-Based Decision Theory (Gilboa and Schmeidler, 1995). Our subjects face a monopoly decision problem about which they have very limited information. Information is presented in a manner which makes similarity judgements according to the feature matching model of Tversky (1977) very plausible. In a 2x2 between-subject design, we vary whether information about the current market is given, in addition to a history, or not. Furthermore, we investigate the impact feedback has on behavior in several independent markets by either giving immediate feedback about obtained profits or not. The results provide some support for the predictions of Case-Based Decision Theory, particularly when no immediate feedback is given. We also find evidence that other simple heuristics are used.

Coordinating Service‐Sensitive Demand and Capacity by Adaptive Decision Making: An Application of Case‐Based Decision Theory*

ABSTRACTThe subject of this article is the simultaneous choice of product price and manufacturing capacity if demand is stochastic and service‐level sensitive. In this setting, capacity as well as price have an impact on demand because several aspects of service level depend on capacity. For example, delivery time will be reduced if capacity is increased given a constant demand rate. We illustrate the relationship between service level, capacity, and demand reaction by a stylized application problem from the after‐sales services industry.The reaction of customers to variations in service level and price is represented by a kinked price‐demand‐rate function. We first derive the optimal price‐capacity combination for the resulting decision problem under full information. Subsequently, we focus on a decision maker (DM) who lacks complete knowledge of the demand function. Hence the DM is unable to anticipate the service level and consequently cannot identify the optimal solution. However, the DM will acquire additional information during the sales process and use it in subsequent revisions of the price‐capacity decision. Thus, this decision making is adaptive and based on experience. In contrast to the literature, which assumes certain repetitive procedures somewhat ad hoc, we develop an adaptive decision process based on case‐based decision theory (CBDT) for the price‐capacity problem. Finally, we show that a CBDT DM in our setting eventually finds the optimal solution, if the DM sets the price based on absorption costs and adequately adjusts the capacity with respect to the observed demand.

Random Walks and Voting Theory

Voters' preferences depend on available information. Following Case-Based Decision Theory, we assume that this information is processed additively. We prove that the collective preferences deduced from the individual ones through majority vote cannot be arbitrary, as soon as a winning quota is required. The proof is based on a new result on random walks.

A Theory of Case‐Based Decisions

Gilboa and Schmeidler's book provides a tremendously enjoyable, clear, and user friendly introduction to a new paradigm for analysing decision making under uncertainty. Case Based Decision Theory (henceforth CBDT) suggests that people make decisions by analogies to past cases, choosing acts that performed well in the past in similar situations, and avoiding acts that performed poorly. The book is spiced with light hearted examples that pave the road for the casual reader to understand the conceptual point of each section without going carefully into the mathematical constructions. Nonetheless, the book contains enough formal content to serve as a complementary textbook in an advanced graduate theory class, and is sprinkled with hints for open questions and directions for future research. Going beyond impressionistic commentary, the book is comprised of three parts: 1. a description of the general (static) model with its axiomatic foundations; 2. a discussion of the conceptual foundations for considering CBDT; and 3. an analysis of CBDT in a dynamic setting that concerns issues of planning, repeated choice, and learning.

Uncertain Decisions: Bridging Theory and Experiments

Part I: Non-Expected Utility Theory. 1. Risk and Uncertainty Aversion with Reference to the Theories of Expected Utility, Rank Dependent Expected Utility, and Choquet Expected Utility A. Montesano. 2. Dynamic Consistency and Non-Expected Utility U. Segal. 3. Ambiguity-Aversion and Non-Additive Probability: Experimental Evidence, Models and Applications C. Camerer. Part II: Non-Expected Utility Applications. 4. Consumption, Savings and Asset Returns with Non-Expected Utility L.G. Epstein. 5. Estimating (Risk) Preference Functionals Using Experimental Methods J.D. Hey. 6. Randomisation, Mixed Strategies and the Reduction Axiom M. Bernasconi. 7. Experimental Studies of Signaling Games F. van Winden. 8. Independence between Events with Non-Additive Probability N. Dimitri. 9. Fuzzy Decision Theory A. Billot. 10. An Overview of Case-Based Decision Theory I. Gilboa, D. Schmeidler. 11. The Recent Advances in Decision Theory under Uncertainty: A Non-Technical Introduction A. Vercelli. Part IV: Value to Real World: The Experimental Contribution. 12. Decision Theory and Real Decisions: A Research Agenda K.R. MacCrimmon. 13. Policy and the Use of Laboratory Experimental Methodology in Economics C.R. Plott. 14. Buyer and Seller Effect Disentangled. An Experiment on the Microstructure of Demand C. Borelli, et al. Index.

Case-based knowledge and induction

Case-based decision theory (CBDT) is a theory of decision-making under uncertainty, suggesting that people tend to choose acts that performed well in similar cases they recall. The theory has been developed from a decision-/game-/economic-theoretical point of view as a potential alternative to expected utility theory (EUT). In this paper, we attempt to reconsider CBDT as a theory of knowledge representation, to contrast it with the rule-based approach, and to study its implications regarding the process of induction.

Expected utility and case-based reasoning

This paper presents an equivalence result between expected utility theory (EU) and a modified version of case-based decision theory (CBDT). To be precise, it shows that a model constructed in EU can be embedded in a CBDT model, and vice versa. CBDT, proposed and axiomatized by Gilboa and Schmeidler (1995, Case-based decision theory, Quarterly Journal of Economics 110, 605–639), is related to case-based reasoning in psychology and artificial intelligence and considered as a descriptive theory of human behavior. In CBDT a decision maker remembers situations similar to the current problem and uses them to help solve it. This idea stems from bounded rationality and is similar in spirit to the satisficing theory of March and Simon (1958, Organizations, Wiley, New York) in the sense that the decision maker tends to satisfice rather than optimize.

Integrating case-based reasoning and decision theory

Case-based reasoning (CBR) and decision-theoretic techniques can be complementary. Decision theory helps CBR deal with uncertainties in the problem domain, while CBR helps decision theory handle complicated problems with many variables. The goal of integrating CBR and decision theory is to improve the ability of CBR systems to solve problems in domains of incomplete information. Our methodology views the retrieval of old cases in CBR as a decision problem, where each case from the case base provides an alternative solution and a prediction of the possible outcomes for the problem. When case-based problem solving encounters uncertainty, our methodology applies decision theory to evaluate each case in terms of the attributes that are significant for the problem, so that the most desirable case can be selected. We implemented our methodology in a case-based design assistant that helps chemists design pharmaceuticals.

Act similarity in case-based decision theory

Case-Based Decision Theory (CBDT) postulates that decision making under uncertainty is based on analogies to past cases. In its original version, it suggests that each of the available acts is ranked according to its own performance in similar decision problems encountered in the past. The purpose of this paper is to extend CBDT to deal with cases in which the evaluation of an act may also depend on past performance of different, but similar acts. To this end we provide a behavioral axiomatic definition of the similarity function over problem-act pairs (and not over problem pairs alone, as in the original model). We propose a model in which preferences are context-dependent. For each conceivable history of outcomes (to be thought of as the “context” of decision) there is a preference order over acts. If these context-dependent preference relations satisfy our consistency-across-contexts axioms, there is an essentially unique similarity function that represents these preferences via the (generalized) CBDT functional.

Act similarity in case-based decision theory

Case-Based Decision Theory (CBDT) postulates that decision making under uncertainty is based on analogies to past cases. In its original version, it suggests that each of the available acts is ranked according to its own performance in similar decision problems encountered in the past. The purpose of this paper is to extend CBDT to deal with cases in which the evaluation of an act may also depend on past performance of different, but similar acts. To this end we provide a behavioral axiomatic definition of the similarity function over problem-act pairs (and not over problem pairs alone, as in the original model). We propose a model in which preferences are context-dependent. For each conceivable history of outcomes (to be thought of as the context of decision) there is a preference order over acts. If these context-dependent preference relations satisfy our consistency-across-contexts axioms, there is an essentially unique similarity function that represents these preferences via the (generalized) CBDT functional.

Case-Based Decision Theory

This paper suggests that decision-making under uncertainty is, at least partly, case-based. We propose a model in which cases are primitive, and provide a simple axiomatization of a decision rule that chooses a “best” act based on its past performance in similar cases. Each act is evaluated by the sum of the utility levels that resulted from using this act in past cases, each weighted by the similarity of that past case to the problem at hand. The formal model of case-based decision theory naturally gives rise to the notions of satisficing decisions and aspiration levels.