Deviations of rational choice: an integrative explanation of the endowment and several context effects (original) (raw)

people's choices are often found to be inconsistent with the assumptions of rational choice theory. over time, several probabilistic models have been proposed that account for such deviations from rationality. However, these models have become increasingly complex and are often limited to particular choice phenomena. Here we introduce a network approach that explains a broad set of choice phenomena. We demonstrate that this approach can be used to compare different choice theories and integrates several choice mechanisms from established models. A basic setup implements bounded rationality, loss aversion, and inhibition in a natural fashion, which allows us to predict the occurrence of well-known choice phenomena, such as the endowment effect and the similarity, attraction, compromise, and phantom context effects. Our results show that this network approach provides a simple representation of complex choice behaviour, and can be used to gain a better understanding of how the many choice phenomena and key theoretical principles from different types of decision-making are connected. The response behaviour of humans on (discrete) choice problems has been extensively studied in many fields of science, such as economics 1-4 , psychology 5-8 , psychometrics 9,10 , cognitive science 11-14 , neuroscience 15,16 , and engineering 17,18. Traditional theories of choice assume the decision-maker as a homo economicus 19,20 , i.e., rational 1,5,21. For choices to be rational all choice alternatives must be comparable and have transitive preference relations, so they can be ordered by the decision-maker. A second feature, and a central principle of rational choice theory, is that a rational decision-maker consistently chooses the outcome that maximises utility, or expected utility for risky or uncertain choices 5,22-24. These assumptions clearly fail the scrutiny of everyday experience. To account for the observed inconsistencies, most models nowadays characterise choice as a probabilistic process 6,9,21,24-29. A prominent group of probabilistic choice models, such as Luce's strict utility model 6,24 and the multinomial logit model 21 for preference, and Bock's nominal categories model 30 for aptitude, are characterised by the following distribution for the choices: in which p S (x) ∈ [0, 1] represents the probably of choosing alternative x from the set of possible alternatives S as a function of the utility of alternative x, exp(π x) , where π x ∈ R. This distribution is also known as the Boltzmann distribution 31,32 from statistical mechanics. For binary choice problems (S = {x, y}) Eq. (1) takes a form known as the Bradley-Terry-Luce model in the decision-making literature 33,34 , or as the Rasch model 9 in psychometrics: (1) p S (x) = exp (π x) y∈S exp π y ,