A Neural Network Model for Attribute‐Based Decision Processes

Usher, Marius; Zakay, Dan

doi:10.1207/s15516709cog1703_2

Cited by 24 publications

(23 citation statements)

References 74 publications

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“…For example, there are some intriguing observations on the effects of frontal lobe damage on decision making (e.g., Damasio, 1994) and a neural network model or two representing decision-making processes at levels that are at least inspired by the nervous system (Holyoak & Simon, 1999;Leven & Levine, 1996;Thagard, 1989;Usher & Zakay, 1993). In addition, Loewenstein (1996) and others recently have begun to reintroduce emotional and visceral factors as key variables that affect decision making (social psychologists would point to K. Lewin, 1935, to Miller, 1944, and perhaps even to Freud, 1923Freud, /1961, for historical precedents).…”

Section: Discussionmentioning

confidence: 99%

Broadening behavioral decision research: Multiple levels of cognitive processing

Medin

Bazerman

1999

Psychonomic Bulletin & Review

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The area of behavioral decision research-specifically, the work on heuristics and biases-has had a tremendous influence on basic research, applied research, and application over the last 25 years. Its unique juxtaposition against economics has provided important benefits, but at the cost of leaving it disconnected from too much of psychology. This paper explores an expanded definition of behavioral decision research through the consideration of multiple levels of cognitive processing. Rather than being limited to how decision makers depart from optimality, we offer a broader analysis of how decision makers define the decision problem and link decisions to goals, as well as a more detailed focus on processes associated with implementing decisions.Over the past few decades, the area of cognitive psychology has grown dramatically, social and developmental psychology have moved strongly in a cognitive direction, and behavioral decision research (BDR) has emerged as a new area of psychology. BDR is unique among psychological subfields in the impact that it has had on research outside of psychology, including its impact on economics, finance, public policy, law,medicine, marketing, organizational behavior, and negotiation. Unfortunately, BDR has also moved farther away from many core areas of psychology, limiting its theoretical development and its integration with advances made in allied areas.Our central thesis is that the most well known part of BDR-the heuristics and biases approach-has been overly constrained by a focus on how people make mistakes at the point of decision. Research on heuristics and biases has implicitly assumed that the goal is known and that the details of implementing decisions are not part of the problem. The prescriptive goal is optimality defined in terms ofthe behavior of rational agents, and, from that perspective, the heuristic and biases literature has yielded a fascinating catalog of human decision errors that is important for both theoretical and practical reasons. A drawback with this orientation, however, is that it tends to define human decision making by what it is not. As a consequence, it does not provide an effective framework for the detailed study ofdecision makers' (often multiple) goals that serve to define a decision context or the linkages between goals and processing issues associated with implementing decisions. We seek to add these components to decision-making analyses, without losing the key strengths of the heuristics and biases approach. This paper overviews the historical development of BDR, highlights a number of challenges to BDR, examines the potential ofconsidering multiple levels ofanalysis to organize new directions for BDR, illustrates the potential for a more psycho-central perspective to decision making within this framework, and provides examples of recent and ongoing research across levels. Collectively, our aim is to build on the strengths of BDR by broadening the framework used to understand how we make decisions. In pursuing this strategy, we are bui...

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Section: Discussionmentioning

confidence: 99%

Broadening behavioral decision research: Multiple levels of cognitive processing

Medin

Bazerman

1999

Psychonomic Bulletin & Review

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“…Leven and Levine (1996) proposed a multiattribute neural network decision model to explain consumer choice behavior; however, it was not applied to any of the major empirical findings on multialternative preferential choice discussed here. Another dynamic model proposed by Usher and Zakay (1993) was designed to implement an EBA choice process using a neural network framework. It provides an explanation for the similarity effect and for some search process results.…”

Section: Previous Artificial Neural Networkmentioning

confidence: 99%

Multialternative decision field theory: A dynamic connectionst model of decision making.

Roe¹,

Busemeyer²,

Townsend³

2001

Psychological Review

704

885

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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.

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“…The similarity effect follows immediately, and fairly uncontroversially, from a stochastic criteria shifting mechanism (Roe et al, 2001;Usher & McClelland, 2004;Usher & Zakay, 1993), a mechanism that has some resemblance to the stochastic examination of choice attributes in Tversky's elimination by aspects (EBA; Tversky, 1972).…”

Section: Attention To Choice Aspects and Temporal Correlationsmentioning

confidence: 99%

Preference reversal in multiattribute choice.

Tsetsos

Usher

Chater

2010

Psychological Review

Self Cite

132

142

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A central puzzle for theories of choice is that people's preferences between options can be reversed by the presence of decoy options (that are not chosen) or by the presence of other irrelevant options added to the choice set. Three types of reversal effect reported in the decision-making literature, the attraction, compromise, and similarity effects, have been explained by a number of theoretical proposals. Yet a major theoretical challenge is capturing all 3 effects simultaneously. We review the range of mechanisms that have been proposed to account for decoy effects and analyze in detail 2 computational models, decision field theory (Roe, Busemeyer, & Townsend, 2001) and leaky competing accumulators (Usher & McClelland, 2004), that aim to combine several such mechanisms into an integrated account. By simulating the models, we examine differences in the ways the decoy effects are predicted. We argue that the LCA framework, which follows on Tversky's relational evaluation with loss aversion (Tversky & Kahneman, 1991), provides a more robust account, suggesting that common mechanisms are involved in both high-level decision making and perceptual choice, for which LCA was originally developed.

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A Neural Network Model for Attribute‐Based Decision Processes

Cited by 24 publications

References 74 publications

Broadening behavioral decision research: Multiple levels of cognitive processing

Broadening behavioral decision research: Multiple levels of cognitive processing

Multialternative decision field theory: A dynamic connectionst model of decision making.

Preference reversal in multiattribute choice.

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