Abstract not found

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.

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: (a) the discovery of less-is-more effects; (b) the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; (c) an advancement from vague labels to computational models of heuristics; (d) the development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an ‘‘adaptive toolbox;’ ’ and (e) the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people’s adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies.

Fund. Nelson Repenning graciously permitted us to administer the tasks in his introductory system dynamics class. We also thank Jim Doyle, Michael Radzicki, Terry Tivnan the referees for helpful comments. Christopher Hunter assisted with data entry.

Human cognition is often biased, from judgments of the time of impact of approaching objects all the way through to estimations of social outcomes in the future. We propose these effects and a host of others may all be understood from an evolutionary psychological perspective. In this article, we elaborate error management theory (EMT; Haselton & Buss, 2000). EMT predicts that if judgments are made under uncertainty, and the costs of false positive and false negative errors have been asymmetric over evolutionary history, selection should have favored a bias toward making the least costly error. This perspective integrates a diverse array of effects under a single explanatory umbrella, and it yields new content-specific predictions. Better safe than sorry. (folk wisdom) Nothing ventured, nothing gained. (contradictory folk wisdom) These two wisdoms seem contradictory. The first urges caution, whereas the second reminds us that we have nothing to lose and should throw caution to the

A suitable project for the new Millenium is to radically reconfigure our image of human rationality. Such a project is already underway, within the Cognitive Sciences, under the umbrellas of work in Situated Cognition, Distributed and Decentralized Cognition, Real-world Robotics and Artificial Life. Such approaches

An evidence accumulation model of forced-choice decision making is proposed to unify the fast and frugal take the best (TTB) model and the alternative rational (RAT) model with which it is usually contrasted. The basic idea is to treat the TTB model as a sequential-sampling process that terminates as soon as any evidence in favor of a decision is found and the rational approach as a sequential-sampling process that terminates only when all available information has been assessed. The unified TTB and RAT models were tested in an experiment in which participants learned to make correct judgments for a set of real-world stimuli on the basis of feedback, and were then asked to make additional judgments without feedback for cases in which the TTB and the rational models made different predictions. The results show that, in both experiments, there was strong intraparticipant consistency in the use of either the TTB or the rational model but large interparticipant differences in which model was used. The unified model is shown to be able to capture the differences in decision making across participants in an interpretable way and is preferred by the minimum description length model selection criterion. A simple but pervasive type of decision requires choosing which of two alternatives has the greater (or the lesser) value on some variable of interest. Examples of

Introduction The perspective taken by Keefer et.al. (forthcoming) focuses on specific decision analysis (DA) methods with a relatively high level of maturity. These include decision trees, influence diagrams as well as multiattribute value trees and utility theory. Since the methods are well known it would be interesting for the readers to reverse the perspective. The point of view could originate from the real problems in the solution of which DA should potentially have a role. One could review the attempts made with any DA like methods. Also, why a particular case was not approached or why was it non-approachable with DA methods but with some others. Today, we may be in a situation where challenging applications, such as dynamic decision problems (see e.g. Virtanen et.al. 1999, 2001), drive new theoretical work, wherefore a reversed perspective would be helpful. From the practitioners' perspective, the selection criterion of Keefer et. al. (forthcoming) for applications seems too re

The CGIAR Systemwide Program on Collective Action and Property Rights (CAPRi) is an initiative of the 15 centers of the Consultative Group on International Agricultural Research (CGIAR). The initiative promotes comparative research on the role of property rights and collective action institutions in shaping the efficiency, sustainability, and equity of natural resource systems. CAPRi’s Secretariat is hosted within the Environment and Production Technology Division (EPDT) of the International Food Policy Research Institute (IFPRI). CAPRi Working Papers contain preliminary material and research results. They are circulated prior to a full peer review to stimulate discussion and critical comment. It is expected that most working papers will eventually be published in some other form and that their content may also be revised

In the first part of this report, four stages of information processing,-- attentional filtering, integration and inference, choice, and response execution,-- are outlined, each of which can be automated. Such automation can vary in its reliability. We distinguish between automation that is perfectly reliable, automation that fails “catastrophically ” and automation whose reliability is high, but understandably imperfect (e.g., drawing inference from inherently ambiguous data or noisy sensors). In the case of imperfect automation, we also distinguish between cases when the operator is and is not aware of the imperfection. We then describe the various human performance costs resulting from these different states and levels of unreliability, as they are relevant to the different stages of automation. We emphasize empirical data from automated attention filtering (Stage 1) in such systems as target cueing alarms, or intelligent information management. Many of these costs relate to the distribution of attention in the environment. In the second part of the report, we then describe a model of the influences on how pilots distribute and allocate visual attention in dynamic environments, in order to maintain situation awareness. The model incorporates bottom up influences on attention allocation related