潜在的連想テスト(Implicit Association Test)のリスク認知への応用

早大学位記番号:新7568早稲田大

Dopamine D_1 Receptors and Nonlinear Probability Weighting in Risky Choice

Misestimating risk could lead to disadvantaged choices such as initiation of drug use (or gambling) and transition to regular drug use (or gambling). Although the normative theory in decision-making under risks assumes that people typically take the probability-weighted expectation over possible utilities, experimental studies of choices among risks suggest that outcome probabilities are transformed nonlinearly into subjective decision weights by a nonlinear weighting function that overweights low probabilities and underweights high probabilities. Recent studies have revealed the neurocognitive mechanism of decision-making under risk. However, the role of modulatory neurotransmission in this process remains unclear. Using positron emission tomography, we directly investigated whether dopamine D_1 and D_2 receptors in the brain are associated with transformation of probabilities into decision weights in healthy volunteers. The binding of striatal D_1 receptors is negatively correlated with the degree of nonlinearity of weighting function. Individuals with lower striatal D_1 receptor density showed more pronounced overestimation of low probabilities and underestimation of high probabilities. This finding should contribute to a better understanding of the molecular mechanism of risky choice, and extreme or impaired decision-making observed in drug and gambling addiction

Qualitatively Coherent Representation Makes Decision-Making Easier with Binary-Colored Multi-Attribute Tables: An Eye-Tracking Study

We aimed to identify the ways in which coloring cells affected decision-making in the context of binary-colored multi-attribute tables, using eye movement data. In our black-white attribute tables, the value of attributes was limited to two (with a certain threshold for each attribute) and each cell of the table was colored either black or white on the white background. We compared the two natural ways of systematic color assignment: “quantitatively coherent” ways and “qualitatively coherent” ways (namely, the ways in which the black-white distinction represented the quantitative amount distinction, and the ways in which the black-white distinction represented the quality distinction). The former consists of the following two types: (Type 1) “larger is black,” where the larger value-level was represented by black, and “smaller is white,” and (Type 2) “smaller is black.” The latter consisted of the following two types: (Type 3) “better is black,” and (Type 4) “worse is black.” We obtained the following two findings. [Result 1] The qualitatively coherent black-white tables (Types 3 and 4) made decision-making easier than the quantitatively coherent ones (Types 1 and 2). [Result 2] Among the two qualitatively coherent types, the “black is better” tables (Type 3) made decision making easier; in fact, the participants focused on the more important (black) cells in the case of “black is better” tables (Type 3) while they did not focus enough on the more important (white) ones in the case of the “white is better” tables (Type 4). We also examined some measures of eye movement patterns and showed that these measures supported our hypotheses. The data showed differences in the eye movement patterns between the first and second halves of each trial, which indicated the phased or combined decision strategies taken by the participants

Avoiding the Worst Decisions: A Simulation and Experiment

Many practical decisions are more realistic concerning preventing bad decisions than seeking better ones. However, there has been no behavioral decision theory research on avoiding the worst decisions. This study is the first behavioral decision research on decision strategies from the perspective of avoiding the worst decisions. We conducted a computer simulation with the Mersenne Twister method and a psychological experiment using the monitoring information acquisition method for two-stage decision strategies of all combinations for different decision strategies: lexicographic, lexicographic semi-order, elimination by aspect, conjunctive, disjunctive, weighted additive, equally weighted additive, additive difference, and a majority of confirming dimensions. The rate of choosing the least expected utility value among the alternatives was computed as the rate of choosing the worst alternative in each condition. The results suggest that attention-based decision rules such as disjunctive strategy lead to a worse decision, and that striving to make the best choice can conversely often lead to the worst outcome. From the simulation and the experiment, we concluded that simple decision strategies such as considering what is most important can lead to avoiding the worst decisions. The findings of this study provide practical implications for decision support in emergency situations.</jats:p

Classifying Decision Strategies in Multi-Attribute Decision-Making: A Multi-Dimensional Scaling and Hierarchical Cluster Analysis of Simulation Data

Previous studies on decision strategies in multi-attribute decision-making (MADM) have primarily relied on computational simulations to assess strategy performance under varying conditions, with particular emphasis on comparisons to the weighted additive rule (WAD) and on evaluations of the cognitive effort required. In contrast, considerably less attention has been devoted to examining the consistency of decision outcomes across different strategies or to developing a systematic classification of strategies based on outcome similarity. To address this gap, the present study investigates the characteristics of decision strategies by analyzing the concordance rates of choices made under identical conditions, along with measures of decision accuracy and information-processing effort. We conducted a hierarchical cluster analysis and applied multi-dimensional scaling (MDS) to a choice concordance matrix derived from simulations using the Mersenne Twister method. In addition, linear multiple regression analyses were performed using the MDS coordinates as predictors of both decision accuracy and cognitive effort. The cluster analysis revealed a primary bifurcation between two major groups: one centered around the Disjunctive (DIS) rule, and another encompassing compensatory strategies such as WAD. Notably, although the Lexicographic (LEX) rule is traditionally considered non-compensatory, it exhibited high similarity in choice patterns to compensatory strategies when assessed via concordance rates. In contrast, DIS-based strategies produced markedly distinct choice patterns