An Information processing theory of human decision making under uncertainty and risk

Computer-based experiments were made to observe and record the verbal behavior of subjects making decisions in quantitative task environments under uncertainty and risk. The subjects exhibited different categories of mental representation of the task environment. These representations form the basis of their search and optimizing techniques.The second phase of the project consisted of generating a psychological theory of the above behavior, in the form of a computer program. Comparing the trace of the program with tape-recorded protocols may indicate the level of completeness of the theory. It was found that the information processing approach can successfully explain general characteristics of decision making behavior and also some individual idiosyncrasies.This terminology is not quite uniform. Most authors in Statistical Decision Theory and Economics have adopted the convention that in a situation of uncertainty there is no à priori information about the system at hand, consequently probability distributions of various alternative outcomes cannot be objectively specified. In cases involving risk, however, it is assumed that, relying on prior events of identical character, these frequency distributions are available. In the present work we have not distinguished between these two cases and Described the psychological state of the subjects as that under uncertainty. The element of risk is represented by the subjects' financial involvement in the outcome of the experiments. The adopted terminology and the distinction made here may be rather useful with experiments which aim at discovering correlation between subjects' behavior and varying pay-off matrices while the stochastic components of the environment stay stationary.The work reported here was done at Carnegie Institute of Technology and was supported by the Advanced Research Projects Agency of the Office of the Secretory of Defense: Contract SD-146.A paper with similar contents was presented at the Fall Joint Computer Conference, Las Vegas, November 1965.     

The neural systems that mediate human perceptual decision making

Perceptual decision making is the act of choosing one option or course of action from a set of alternatives on the basis of available sensory evidence. Thus, when we make such decisions, sensory information must be interpreted and translated into behaviour. Neurophysiological work in monkeys performing sensory discriminations, combined with computational modelling, has paved the way for neuroimaging studies that are aimed at understanding decision-related processes in the human brain. Here we review findings from human neuroimaging studies in conjunction with data analysis methods that can directly link decisions and signals in the human brain on a trial-by-trial basis. This leads to a new view about the neural basis of human perceptual decision-making processes.     

Regime changes in ecological systems: an information theory approach

We present our efforts at developing an ecological system index using information theory. Specifically, we derive an expression for Fisher Information based on sampling of the system trajectory as it evolves in the space defined by the state variables of the system, i.e. its state space. The Fisher Information index, as we have derived it, is a measure of system order, and captures the characteristic variation in speed and acceleration along the system's periodic steady-state trajectories. When calculated repeatedly over the system period, this index tracks steady states and transient behavior. We believe that such an index could be useful in detecting system 'flips' associated with a regime change, i.e. determining when systems are in a transient between one steady state and another. We illustrate the concepts using model ecosystems.     

The importance of urgency in decision making based on dynamic information

A standard view in the literature is that decisions are the result of a process that accumulates evidence in favor of each alternative until such accumulation reaches a threshold and a decision is made. However, this view has been recently questioned by an alternative proposal that suggests that, instead of accumulated, evidence is combined with an urgency signal. Both theories have been mathematically formalized and supported by a variety of decision-making tasks with constant information. However, recently, tasks with changing information have shown to be more effective to study the dynamics of decision making. Recent research using one of such tasks, the tokens task, has shown that decisions are better described by an urgency mechanism than by an accumulation one. However, the results of that study could depend on a task where all fundamental information was noiseless and always present, favoring a mechanism of non-integration, such as the urgency one. Here, we wanted to address whether the same conclusions were also supported by an experimental paradigm in which sensory evidence was removed shortly after it was provided, making working memory necessary to properly perform the task. Here, we show that, under such condition, participants’ behavior could be explained by an urgency-gating mechanism that low-pass filters the mnemonic information and combines it with an urgency signal that grows with time but not by an accumulation process that integrates the same mnemonic information. Thus, our study supports the idea that, under certain situations with dynamic sensory information, decisions are better explained by an urgency-gating mechanism than by an accumulation one.     

Modeling hunter-gatherer decision making: complementing optimal foraging theory

While optimal foraging theory has been of considerable value for understanding hunter-gatherer subsistence patterns, there is a need for a complementary approach to human foraging behavior which focuses on decision-making processes. Having made this argument, the paper proposes the type of modeling approach that should be developed, using decision making during encounter foraging as an example. This model concerns the individual decision maker attempting to improve his foraging efficiency, rather than maximize it, under the constraint of limited information and with conflicting goals. This is illustrated by applying it to the Valley Bisa hunters using computer simulation.     

Quantum-like model of cognitive decision making and information processing

In this paper we offer the quantum-like (QL) representation of the Shafir–Tversky statistical effect which is well known in cognitive psychology. We apply the so-called contextual approach. We consider the Shafir–Tversky effect to result from mixing statistical data obtained in incompatible contexts which are involved, e.g. in Prisoner’s Dilemma or in more general games in which the disjunction effect can be found. As a consequence, the law of total probability is violated for the experimental data obtained in experiments on cognitive psychology by Shafir and Tversky [Shafir, E., Tversky, A., 1992. Thinking through uncertainty: nonconsequential reasoning and choice. Cogn. Psychol. 24, 449–474] as well as Tversky and Shafir [Tversky, A., Shafir, E., 1992. The disjunction effect in choice under uncertainty. Psychol. Sci. 3, 305–309]. Moreover, we can find a numerical measure of contextual incompatibility (the so-called coefficient of interference) as well as represent contexts which are involved in Prisoner’s Dilemma (PD) by probability amplitudes—normalized vectors (“mental wave functions”). We remark that statistical data from Shafir and Tversky [Shafir, E., Tversky, A., 1992. Thinking through uncertainty: nonconsequential reasoning and choice. Cogn. Psychol. 24, 449–474] and Tversky and Shafir [Tversky, A., Shafir, E., 1992. The disjunction effect in choice under uncertainty. Psychol. Sci. 3, 305–309] experiments differ crucially from the point of view of mental interference. The second one exhibits the conventional trigonometric (cos?$\cos ?$-type) interference while the first one exhibits even the so-called hyperbolic (cosh?$\cosh ?$-type) interference. We discuss the QL processing of information by cognitive systems, especially, the QL decision making and both classical and QL rationality and ethics.     

Principal/agent theory and decision making in health care

Principal/agent theory, an economics concept that defines an agency relationship as "a contract under which one or more persons engage another person (the agent) to perform some service on their behalf which involves delegating some decision-making authority to the agent," is held to be applicable to the patient/physician relationship, in contrast to the view that this is a fiduciary relationship, not a contractual one. The distinction between fiduciary and contractual relationships is illuminated in the process of evaluating objections to the application of the principal/agent theory to the patient/physician relationship. The ability of the principal/agent theory to reveal important features of advance directives, understood as public documents rather than as confidential instructions from patient to physician, is also examined.     

Flutter discrimination: neural codes,perception, memory and decision making

Recent studies combining psychophysical and neurophysiological experiments in behaving monkeys have provided new insights into how several cortical areas integrate efforts to solve a vibrotactile discrimination task. In particular, these studies have addressed how neural codes are related to perception, working memory and decision making in this model. The primary somatosensory cortex drives higher cortical areas where past and current sensory information are combined, such that a comparison of the two evolves into a behavioural decision. These and other observations in visual tasks indicate that decisions emerge from highly-distributed processes in which the details of a scheduled motor plan are gradually specified by sensory information.     

Application of robust optimization in matrix-based LCI for decision making under uncertainty

Purpose

When product systems are optimized to minimize environmental impacts, uncertainty in the process data may impact optimal decisions. The purpose of this article is to propose a mathematical method for life cycle assessment (LCA) optimization that protects decisions against uncertainty at the life cycle inventory (LCI) stage.

Methods

A robust optimization approach is proposed for decision making under uncertainty in the LCI stage. The proposed approach incorporates data uncertainty into an optimization problem in which the matrix-based LCI model appears as a constraint. The level of protection against data uncertainty in the technology and intervention matrices can be controlled to reflect varying degrees of conservatism.

Results and discussion

A simple numerical example on an electricity generation product system is used to illustrate the main features of this methodology. A comparison is made between a robust optimization approach, and decision making using a Monte Carlo analysis. Challenges to implement the robust optimization approach on common uncertainty distributions found in LCA and on large product systems are discussed. Supporting source code is available for download at https://github.com/renwang/Robust_Optimization_LCI_Uncertainty.

Conclusions

A robust optimization approach for matrix-based LCI is proposed. The approach incorporates data uncertainties into an optimization framework for LCI and provides a mechanism to control the level of protection against uncertainty. The tool computes optimal decisions that protects against worst-case realizations of data uncertainty. The robust optimal solution is conservative and is able to avoid the negative consequences of uncertainty in decision making.     

MAPPIN'SDM--the multifocal approach to sharing in shared decision making

Background

The wide scale permeation of health care by the shared decision making concept (SDM) reflects its relevance and advanced stage of development. An increasing number of studies evaluating the efficacy of SDM use instruments based on various sub-constructs administered from different viewpoints. However, as the concept has never been captured in operable core definition it is quite difficult to link these parts of evidence.This study aims at investigating interrelations of SDM indicators administered from different perspectives.

Method

A comprehensive inventory was developed mapping judgements from different perspectives (observer, doctor, patient) and constructs (behavior, perception) referring to three units (doctor, patient, doctor-patient-dyad) and an identical set of SDM-indicators. The inventory adopted the existing approaches, but added additional observer foci (patient and doctor-patient-dyad) and relevant indicators hitherto neglected by existing instruments. The complete inventory comprising a doctor-patient-questionnaire and an observer-instrument was applied to 40 decision consultations from 10 physicians from different medical fields. Convergent validities were calculated on the basis of Pearson correlation coefficients.

Results

Reliabilities for all scales were high to excellent. No correlations were found between observer and patients or physicians neither for means nor for single items. Judgements of doctors and patients were moderately related. Correlations between the observer scales and within the subjective perspectives were high. Inter-perspective agreement was not related to SDM performance or patient activity.

Conclusion

The study demonstrates the contribution to involvement made by each of the relevant perspectives and emphasizes the need for an inter-subjective approach regarding SDM measurement.     

Choosing the greater of two goods: neural currencies for valuation and decision making

To make adaptive decisions, animals must evaluate the costs and benefits of available options. The nascent field of neuroeconomics has set itself the ambitious goal of understanding the brain mechanisms that are responsible for these evaluative processes. A series of recent neurophysiological studies in monkeys has begun to address this challenge using novel methods to manipulate and measure an animal's internal valuation of competing alternatives. By emphasizing the behavioural mechanisms and neural signals that mediate decision making under conditions of uncertainty, these studies might lay the foundation for an emerging neurobiology of choice behaviour.