Robust interactive decision-analysis (RID): Behavioral results and implications

We have proposed a novel interactive procedure for performing decision analysis, called Robust Interactive Decision Analysis (RID), which permits a decision maker (DM) to voluntarily and interactively express strong (viz, sure) binary preferences for actions, partial decision functions, and full decision functions, and only imprecise probability and utility function assessments. These serve as INPUTS TO operators to prune the state probability space and decision space until an optimal choice strategy is obtained. The viability of the RID approach depends on a DM's ability to provide such information consistently and meaningfully. On a limited scale we experimentally investigate the behavioral implications of the RID method in order to ascertain its potential operational feasibility and viability. More specifically, we examine whether a DM can (1) express strong preferences between pairs of vectors of unconditional and conditional payoffs or utilities consistently; (2) provide imprecise (ordinal and interval) state probabilities that are individually as well as mutually consistent with the state probabilities imputed from the expressed strong preferences. The results show that a DM can provide strong individually and mutually consistent preference and ordinal probability information. Moreover, most individuals also appear to be able to provide interval probabilities that are individually and mutually consistent with their strong preference inputs. However, the several violations observed, our small sample size, and the limited scope of our investigation suggest that further experimentation is needed to determine whether and/or how such inputs should be elicited. Overall, the results indicate that the RID method is behaviorally viable.     

Psychological determinants of decision attitude

Decision attitude — an analog of risk attitude — is the propensity to make (or avoid making) a decision: in decision aversion, a person finds it more desirable to receive through fiat the better of two options than to have a choice between them; in decision seeking, the choice is more desirable, even though it can lead to nothing better than the best option. Both decision aversion and decision seeking were found in hypothetical scenarios. Experimental manipulations and subjects' justifications point to anticipated regret, fear of blame for poor outcomes, and desire for equitable distributions as sources of decision aversion. One source of decision seeking (for self) and decision aversion (when deciding for others) appears to be the desire for the self-determination of the affected parties. We consider the implications of our results for personal choice and public policy decisions.     

Decisions,actions, and the appropriateness of confidence in knowledge

This research examines the appropriateness of confidence (i.e., subjective probability judgments) in knowledge associated with decisions and actions of social importance. One hundred and thirty seven participants completed a 50 item questionnaire assessing their knowledge of the two leading candidates in the 1988 presidential election in the U.S.A. Ninety one of the respondents completed the questionnaire one week prior to the election, whereas the other 46 completed the questionnaire on election day shortly after voting. After each item in the questionnaire, all respondents indicated whether or not the item content represented a reason why they voted (or intended to vote) for or against the candidate to whom the item referred. Within-person results indicated that, in comparison to items that were not cited as reasons for voting intentions or voting behavior, items endorsed as reasons were characterized by better accuracy and resolution, but worse overconfidence. Between groups, decision makers were significantly more accurate and better calibrated than those who had not made a decision between the candidates. Implications of inappropriate confidence on decision making effectiveness are discussed.     

Do medical decision analyses' largest gains grow from the smallest trees?

Some decision analysts recommend reserving their models for careful, complete study of complex problems. Others believe simple problem analyses provide the greatest gains. To clarify these issues, I tested: (1) whether simple published analyses compared to complex ones, produced results as non-intuitive; and (2) whether non-intuitiveness represented analytic gain (or equivalently, intuitive loss). Very limited data also examined (3) the gain from adding model intricacy for a given problem vs. selecting more complex problems for analysis. To assess two non-intuitiveness measures, 75 subjects (33 physicians and 42) non-physicians stated their intuitive preferences in 40 (22 published and 18 unpublished) medical dilemmas. For both physicians and non-physicians, simpler models (those with lees than 30 terminal branches in the decision tree) had about one third the non-intuitiveness of more complex ones. Three tests also supported the premise that analyses outperform intuition, therefore that on average the formers' non-intuitiveness reflects less analytic gain. In addition, for different models of the same problem, tree complexity did not correlate with gain. Thus, simpler trees may not generally gain less because they inadequately describe problems. Instead, simpler analyses may represent simpler problems, with more similar intuitive and formal solutions. If so, these findings may help us avoid some costly but unnecessary simple analyses.     

Decision Support System and Multi‐Criteria Decision Aid: A State of the Art and Perspectives

The aim of this paper is to highlight the role of the Decision Support System within the field of multi‐criteria decision aid (MCDA). The MCDA tools have been incorporated into systems to create Multi‐Criteria Decision Support Systems (MCDSSs). In our literature review, we noticed that more than 100 papers have been written over a 20‐year period in which MCDSS was used as a decision‐making tool. The present paper describes some real applications of MCDSS in different fields, harmoniously combined with decision‐making methods such as analytic hierarchy process, Utility Additive, and Goal Programming. The present study proposes an integrative MCDSS evaluation through guidance on the tools most useful for a specific user with a particular decision problem. Copyright © 2014 John Wiley & Sons, Ltd.     

Discerning Mouse Trajectory Features With the Drift Diffusion Model

Mouse tracking, a new action-based measure of behavior, has advanced theories of decision making with the notion that cognitive and social decision making is fundamentally dynamic. Implicit in this theory is that people's decision strategies, such as discounting delayed rewards, are stable over task design and that mouse trajectory features correspond to specific segments of decision making. By applying the hierarchical drift diffusion model and the Bayesian delay discounting model, we tested these assumptions. Specifically, we investigated the extent to which the “mouse-tracking” design of decision-making tasks (delay discounting task, DDT and stop-signal task, SST) deviate from the standard “keypress” design of decision making tasks. We found remarkable agreement in delay discounting rates (intertemporal impatience) obtained in the keypress and mouse-tracking versions of DDT (ρ = 0.90) even though these tasks were given about 1 week apart. Rates of evidence accumulation converged well in the two versions (DDT, ρ = .86; SST, ρ = .55). Omission/commission error in SST showed high agreement (ρ = .42, ρ = .53). Mouse-motion features such as maximum velocity and AUC (area under the curve) correlated well with nondecision time (ρ = −.42) and boundary separation (ρ = .44)—the amount of information needed to accumulate prior to making a response. These results indicate that the response time (RT) and motion-based decision tasks converge well at a fundamental level, and that mouse-tracking features such as AUC and maximum velocity do indicate the degree of decision conflict and impulsivity.     

Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.     

问题性网络游戏使用青少年未来取向的助推研究

摘要：以问题性网络游戏使用青少年为研究对象,基于锚定效应助推国民身心健康的理论前提和态度改变理论视角,通过实验1和实验2分别建立未来取向内部锚和未来取向外部锚,检验未来取向内部锚和外部锚对减少问题性网络游戏使用青少年游戏时间的有效性。结果表明,未来取向内部和外部的助推方式,均能够有效促进问题性网络游戏使用青少年游戏时间的减少,提高了决策效用。     

Selecting a new technology strategy using multiattribute,multilevel decision trees

Research indicates that selecting a strategy to best exploit a new technology is a complex decision-making process. The task involves making a series of decisions with multiple alternatives, each to be evaluated by multiple criteria whose values have high levels of uncertainty. This paper presents a methodology for modelling a new technology decision using decision trees and an optimizing algorithm. A problem of a mining company considering the adoption of new technology is used to illustrate the decision-making task and modelling methodology. A numerical solution to the case demonstrates the potential of the optimizing technique in strategy selection.     

Preference structures and threshold models

Preferences are often represented in terms of a function, in the deterministic case as well as in the probabilistic case. In the present paper we develop a new numerical representation of preference structures for which the strict preference relation (P) is without circuit but not necessarily transitive. Moreover, we investigate the consequences of the representation for the usual preference structures. In particular, we propose new formulations for the numerical representation of the interval order structure.