Abstract not found

In many theories of decision under risk (e.g., expected utility theory, rank dependent utility theory, and prospect theory) the utility or value of a prospect is independent of other prospects or options in the choice set. The experiments presented here show a large effect of the available options set, suggesting instead that prospects are valued relative to one another. The judged certainty equivalent is strongly influenced by the options available. Similarly, the selection of a preferred option from a set of prospects is strongly influenced by the prospects available. Alternative theories of decision under risk (e.g., the stochastic difference model, multialternative decision field theory, and range frequency theory), where prospects themselves or prospect attributes are valued relative to one another, can provide an account of these context effects.

Almost all models of simple and choice response time (RT) employ a stochastic (i.e., variable within trial) accumulation decision process. In order to account for the relationship between correct and error choice RT, it has been found necessary to also include between trial variability in the starting point and/or the rate of accumulation, both in linear (Ratcliff & Rouder, 1998) and nonlinear (Usher & McClelland, 2001) stochastic models. We show that a ballistic (i.e., deterministic within trial) model using a simplified version of Usher and McClellands nonlinear accumulation process, and assuming only between trial variability in the rate and starting point of accumulation, is not only capable of accounting for the relationship between error and correct RT, but can also model other benchmark behavioural phenomena, such as RT distribution and speed-accuracy trade off. We successfully fit our ballistic model to Ratcliff and Rouders data, which exhibit many of the benchmark phenomena. Even for fast and easy decisions, a simple summation of sensory and motor transduction delays and conduction times in the nervous system cannot account for the duration and variability of reaction times. (Hanes & Schall, 1996, p.427). The slowness and variability of response time (RT) has been almost universally explained by decision processes involving stochastic accumulation of information. Stochastic models assume that the accumulated information varies randomly from moment to moment during the decision process. RT is relatively slow because a criterion amount of information must be accumulated before a response is made, and RT ...

The roles of loss aversion and inhibition among alternatives are examined in models of the similarity, compromise, and attraction effects that arise in choices among 3 alternatives differing on 2 attributes. R. M. Roe, J. R. Busemeyer, and J. T. Townsend (2001) have proposed a linear model in which effects previously attributed to loss aversion (A. Tversky & D. Kahneman, 1991) arise from attention switching between attributes and similarity-dependent inhibitory interactions among alternatives. However, there are several reasons to maintain loss aversion in a theory of choice. In view of this, an alternative theory is proposed, integrating loss aversion and attention switching into a nonlinear model (M. Usher & J. L. McClelland, 2001) that relies on inhibition independent of similarity among alternatives. The model accounts for the 3 effects and makes testable predictions contrasting with those of the Roe et al. (2001) model. Several interesting empirical discoveries have emerged from studies of how people choose between several objects that differ on two or more attributes. For example, someone might be given a choice among three automobiles, varying in performance quality and driving economy (Roe, Busemeyer, & Townsend, 2001).

Abstract not found

We present a theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales. Instead, we assume that an attribute’s subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory and is its rank within the sample. We assume that the sample reflects both the immediate distribution of attribute values from the current decision’s context and also the background, real-world distribution of attribute values. DbS accounts for concave utility functions; losses looming larger than gains; hyperbolic temporal discounting; and the overestimation of small probabilities and the underestimation of large probabilities.

In this article, the first explicit, theory-based comparison of 2-choice and go/no-go variants of 3 experimental tasks is presented. Prior research has questioned whether the underlying core-information processing is different for the 2 variants of a task or whether they differ mostly in response demands. The authors examined 4 different diffusion models for the go/no-go variant of each task along with a standard diffusion model for the 2-choice variant (R. Ratcliff, 1978). The 2-choice and the go/no-go models were fit to data from 4 lexical decision experiments, 1 numerosity discrimination experiment, and 1 recognition memory experiment, each with 2-choice and go/no-go variants. The models that assumed an implicit decision criterion for no-go responses produced better fits than models that did not. The best model was one in which only response criteria and the nondecisional components of processing changed between the 2 variants, supporting the view that the core information on which decisions are based is not different between them.

Appropriate trust in and reliance on automation are critical for safe and efficient system operation. This paper fills an important research gap by describing a quantitative model of trust in automation. We extend decision field theory (DFT) to describe the multiple sequential decisions that characterize reliance on automation in supervisory control situations. Extended DFT (EDFT) represents an iterated decision process and the evolution of operator preference for automatic and manual control. The EDFT model predicts trust and reliance, and describes the dynamic interaction between operator and automation in a closed-loop fashion: the products of earlier decisions can transform the nature of later events and decisions. The simulation results show that the EDFT model captures several consistent empirical findings, such as the inertia of trust and the nonlinear characteristics of trust and reliance. The model also demonstrates the effects of different types of automation on trust and reliance. It is possible to expand the EDFT model for multioperator multiautomation situations.

During the last 25 years, prospect theory and its successor, cumulative prospect theory, replaced expected utility as the dominant descriptive theories of risky decision making. Although these models account for the original Allais paradoxes, 11 new paradoxes show where prospect theories lead to self-contradiction or systematic false predictions. The new findings are consistent with and, in several cases, were predicted in advance by simple “configural weight ” models in which probability-consequence branches are weighted by a function that depends on branch probability and ranks of consequences on discrete branches. Although they have some similarities to later models called “rank-dependent utility, ” configural weight models do not satisfy coalescing, the assumption that branches leading to the same consequence can be combined by adding their probabilities. Nor do they satisfy cancellation, the “independence ” assumption that branches common to both alternatives can be removed. The transfer of attention exchange model, with parameters estimated from previous data, correctly predicts results with all 11 new paradoxes. Apparently, people do not frame choices as prospects but, instead, as trees with branches.

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