Systems analysis and dynamic decision making

Previous research in dynamic decision making has failed to produce a unified approach to the area This has result in a collection of experiments whose results are difficult to generalise from the specific task systems used. It is argued that an approach focusing on the structure of the task system and employing well-known methods of systems analysis can overcome this difficulty. In addition, this technique can order research and assist in the psychological interpretation of system parameters.The use of systems analysis in this role is demonstrated in an experiment using first order feedback system. subjects were required to stabilise the system at a level other than its equilibrium. The results show that subjects quickly adapt to different values of parameters in the system boundary. However performance was consistently better in more inertial systems. This suggests that subjects rely on the task system to integrate over-and underadjustments of their decision inputs.     

Misperceptions of feedback in dynamic decision making

In recent years laboratory experiments have shed significant light on the behavior of economic agents in a variety of microeconomic and decision-theoretic contexts such as auction markets, portfolio choice, and preference elicitation. Despite the success of experimental techniques in the micro domain, there has been relatively little work linking the behavior of decision makers to the dynamics of larger organizations such as corporations, industries, or the macroeconomy. This paper presents a laboratory experiment in which subjects manage a simulated economy. Subjects must invest sufficient capital plant and equipment to satisfy demand. Subjects were given complete and perfect information regarding the structure of the simulated economy, the values of all variables, and the past history of the system. Nevertheless, the overwhelming majority of the subjects generate significant and costly oscillations. A simple decision rule based on the anchoring and adjustment heuristic is shown to simulate the subjects' decisions quite well. Several distinct sources of the subjects' poor performance are identified and termed “misperceptions of feedback.” The decision rule is related to various models of economic fluctuations; implications for experimental investigation of dynamic decision making in aggregate systems are explored.     

The effects of feedback format on dynamic decision making

The tendency for people to perform poorly on dynamic decision-making tasks has been attributed to their inability to form adequate mental models of dynamic systems (Sterman, 1994). An alternative explanation is that the tabular feedback formats used in previous studies do not facilitate mental representations of the dynamic task and lead to processing overload. Drawing on research in the human factors area, this study examined the impact of graphical versus tabular feedback formats on performance, response times, and learning on a dynamic inventory control task under differing levels of complexity. The graphical feedback group performed better but the tabular group showed stronger evidence of learning. The two groups did not differ in response times. Records of decision rationales showed that participants were aware of complexity factors but were unable to cope with them effectively under either feedback format. The proximity compatibility principle ( Wickens & Carswell, 1995) from human factors, which argues that feedback must be matched to the processing requirements of the task, provides a useful theoretical framework for the design and testing of feedback formats for organizational tasks and criteria.     

Instance‐based learning in dynamic decision making

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic‐based to instance‐based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT's learning mechanisms have been implemented in an ACT‐R cognitive model. Through a series of experiments, this paper shows how the IBLT's learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.     

Multialternative decision field theory: a dynamic connectionist model of decision making

The authors interpret decision field theory (J. R. Busemeyer & J. T. Townsend, 1993) as a connectionist network and extend it to accommodate multialternative preferential choice situations. This article shows that the classic weighted additive utility model (see R. L. Keeney & H. Raiffa, 1976) and the classic Thurstone preferential choice model (see L. L. Thurstone, 1959) are special cases of this new multialternative decision field theory (MDFT), which also can emulate the search process of the popular elimination by aspects (EBA) model (see A. Tversky, 1969). The new theory is unique in its ability to explain several central empirical results found in the multialternative preference literature with a common set of principles. These empirical results include the similarity effect, the attraction effect, and the compromise effect, and the complex interactions among these three effects. The dynamic nature of the model also implies strong testable predictions concerning the moderating effect of time pressure on these three effects.     

The roles of planning,learning, and mental models in repeated dynamic decision making

Repeated search and decision making is a common consumer activity that should benefit from advanced planning. In three simulated shopping experiments, we find that people often fail to plan spontaneously or, when they do plan, do not use an appropriate mental model of the search problem. We also manipulate the mental models used by subjects and find that while our manipulation successfully encourages the development of appropriate mental models and improves performance when search costs are low, it does not result in the type of sophisticated mental model required to change strategies based on increased search costs. Finally, we show that the benefits of planning generalize to real world shopping behavior in a field experiment.     

A neural cognitive model of argumentation with application to legal inference and decision making

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data).     

The effect of constant outcome value on judgments and decision making given linguistic probabilities

Two experiments were performed to determine whether judgments of the relative chances of two independent events occurring are biased by constant outcome values contingent on the events when the uncertainties are specified by linguistic expressions (e.g. doubtful). In Experiment 1, subjects directly judged the relative chances of the two events, of which one was represented by a spinner and the other by a linguistic probability expression. In Experiment 2, only linguistic probability expressions were used to describe the two events and a betting procedure was used. A bias was evident in both studies, such that the relative judgments tended to favour the event with the positive rather than the negative contingent outcome. The bias was smaller for the low- than for the high-probability phrases. Individual differences were great, with the bias appearing strongly in only about one-third of the population. Theoretical implications of the present and related results are discussed.     

Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates

Recent work has given rise to the view that reward-based decision making is governed by two key controllers: a habit system, which stores stimulus-response associations shaped by past reward, and a goal-oriented system that selects actions based on their anticipated outcomes. The current literature provides a rich body of computational theory addressing habit formation, centering on temporal-difference learning mechanisms. Less progress has been made toward formalizing the processes involved in goal-directed decision making. We draw on recent work in cognitive neuroscience, animal conditioning, cognitive and developmental psychology, and machine learning to outline a new theory of goal-directed decision making. Our basic proposal is that the brain, within an identifiable network of cortical and subcortical structures, implements a probabilistic generative model of reward, and that goal-directed decision making is effected through Bayesian inversion of this model. We present a set of simulations implementing the account, which address benchmark behavioral and neuroscientific findings, and give rise to a set of testable predictions. We also discuss the relationship between the proposed framework and other models of decision making, including recent models of perceptual choice, to which our theory bears a direct connection.     

决策与判断中的分解效应

分解效应是人类主观判断中的一种较稳固的行为偏差,并且判断结果会对随后的决策产生重要影响,因此,对该领域研究成果的全面梳理具有重要的理论意义和实际价值。本文主要介绍了支持理论中的分解效应,总结梳理了概率判断与时间判断中分解效应的研究,综述了其他社会判断中的分解效应研究成果,展望了决策与判断中的分解效应的未来研究方向。通过上述内容的阐述,希望能为该领域研究者提供新的思路,推动国内相关领域研究的发展。     

The semantic side of decision making

The research reported in this paper follows the perspective that decision making is a meaningful act that conveys information. Furthermore, the potential meanings associated with decision options may affect the decisions themselves. This idea is examined in the contexts of compensation, donation, and exchange. In general, judgments were relation dependent and meaning dependent. Furthermore, the results show nonmonotonicities and limited substitutability in a pattern that challenges straightforward ways of mapping decisions onto a common currency of utility.     

Knowledge management in decision making and decision support

A knowledge-oriented view of decisions and decision making is introduced, as a complement to classical perspectives and as a contribution of under-standing computer-based possibilities for relaxing strains on decision makers. This perspective includes a model of knowledge management activities performed by a decision maker and a taxonomy of knowledge types. It leads to a characterization of decision-support-system purpose, traits, and potentials that offers a basis for new research into computer-based possibilities for knowledge management. Clyde W. Holsapple holds the Rosenthal Endowed Chair in Management Information Systems and is Professor of Decision Science and Information Systems at the College of Business and Economics, University of Kentucky.     

Team confidence,motivated information processing,and dynamic group decision making

According to the Motivated Information Processing in Groups (MIP‐G) model, groups should perform ambiguous (non‐ambiguous) tasks better when they have high (low) epistemic motivation and concomitant tendencies to engage in systematic (heuristic) information processing and exchange. The authors tested this prediction in an experiment with four‐person groups performing a complex and dynamic decision making task. Group confidence was measured after extensive training and prior to actual group decision‐making. Task ambiguity was manipulated. Results showed that when task ambiguity was low, group confidence indeed benefits decision quality and group performance. But when task ambiguity was high, group confidence hurt decision quality and group performance. Implications for theory and practice are discussed. Copyright © 2010 John Wiley & Sons, Ltd.     

The majority rule in individual decision making

This research investigates an understudied decision heuristic, the majority rule. By using the rule, decision makers choose the option superior on most of the available cues. Cues are broadly defined, including advisors and attributes. We propose that decision makers are more likely to use the majority rule when encouraged to employ intra-cue comparison as opposed to intra-option integration, and that their choices are influenced by factors that influence which option appears majority superior. We corroborate the two propositions in four studies. In Studies 1 and 2, we explore two factors that moderate use of the majority rule through facilitating intra-cue comparison or intra-option integration—response mode and information display format. In Studies 3 and 4, we explore two factors that influence choice through influencing which option appears majority-superior—cue-unpacking and cue-regrouping.     

Neuro-cognitive mechanisms of decision making in joint action: a human-robot interaction study

In this paper we present a model for action preparation and decision making in cooperative tasks that is inspired by recent experimental findings about the neuro-cognitive mechanisms supporting joint action in humans. It implements the coordination of actions and goals among the partners as a dynamic process that integrates contextual cues, shared task knowledge and predicted outcome of others’ motor behavior. The control architecture is formalized by a system of coupled dynamic neural fields representing a distributed network of local but connected neural populations. Different pools of neurons encode task-relevant information about action means, task goals and context in the form of self-sustained activation patterns. These patterns are triggered by input from connected populations and evolve continuously in time under the influence of recurrent interactions. The dynamic model of joint action is evaluated in a task in which a robot and a human jointly construct a toy object. We show that the highly context sensitive mapping from action observation onto appropriate complementary actions allows coping with dynamically changing joint action situations.     

The role of attitudes in memory-based decision making

Frequently, considerable knowledge of the attributes of decision alternatives is available in memory so as to permit a thoughtful and deliberate choice. However, in many instances, individuals neglect to use such knowledge and instead rely on "attitude-based" strategy to make a memory-based decision. The findings from two experiments suggest that as to the motivation to make a correct decision or the opportunity to use the available attribute knowledge decreases, the likelihood that attitudes will guide a memory-based decision increases. The findings illustrate the functional role attitudes play in guiding decisions and behavior. By providing a ready means of evaluating choice alternatives, attitudes enable an individual to make a decision relatively quickly and effortlessly.