The practical application of this prescriptive approach how people ought to make decisions is called decision analysisand is aimed at finding tools, methodologies and software decision support systems to help people make better decisions. In contrast, positive or descriptive decision theory is concerned with describing observed behaviors under the assumption that the decision-making agents are behaving under some consistent rules. These rules may, for instance, have a procedural framework e.
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In our everyday life we often have to make decisions with uncertain consequences, for instance in the context of investment decisions.
To successfully cope with these situations, the nervous system has to be able to estimate, represent, and eventually resolve uncertainty at various levels. That is, not only are there different forms of uncertainty with different consequences for behavior and learning but research indicates that the processing of uncertainty highly depends on situation and context.
The present research topic includes both review and original research articles that seek to shed light on the neural processes underlying decision making under uncertainty with a particular focus on situational and contextual influences.
First, Bland and Schaefer review the diverse and often overlapping definitions of uncertainty. They identify three main forms—expected uncertainty including riskunexpected uncertainty and volatility—and review theoretical and empirical evidence that supports this dissociation.
Several original research articles then aim to either directly compare different forms of uncertainty or to identify further dissociations within these forms. Payzan-LeNestour and Bossaerts systematically vary unexpected and estimation uncertainty to study what drives exploration as opposed to exploitation.
The next set of papers explores situational and contextual aspects of expected uncertainty. First, Studer et al. Using eye-tracking data they observe task-dependent attentional shifts from probabilities to amounts which may influence the neural computation of value.
Consequently, individuals often chose options with higher probabilities but place higher bids on options with higher amounts. They find that brain activity in a network previously related to risk increases as individuals continue to inflate a balloon—thus, increasing their risk—while activity in a value-related brain region decreases at the same time.
They argue that different neural systems indicate different neural and psychological processes for risk-taking in gains and losses.
Finally, Heilbronner and Hayden round off this set of papers by providing an account of risk-seeking behavior. While risk-seeking is usually observed in only a minority of human study participants, it is the dominant form of risk preference observed in monkey studies.
Finally, a third set of papers represents an increasingly fertile area of research by connecting risk-taking to the social contexts and affective processes underlying behavior. They find that at low probabilities subjects are less risk taking for own decisions as opposed to high probabilities where the effect is reversed.
This difference in preferences toward risk is underlined by partially distinct neural networks that are recruited when choosing for oneself or for others. Using a model-based approach, Zhu et al. This effect was further enhanced by the presence of uncertainty.
Finally, Wu et al.Hojjat Ghaderi, University of Toronto 1 CSC Intro to Artificial Intelligence Decision Making Under Uncertainty Decision Trees DBN: and Decision Network: ,,, Hojjat Ghaderi, University of Toronto 2 Preferences. Written by the distinguished creator of new decision theories Itzhak Gilboa, Theory of Decision under Uncertainty is a beautifully written critical account of decision theory that answers these and other important questions/5(3).
The Center for Decision Making under Uncertainty assesses the depth and breadth of uncertainty and risk levers in policy domains and research pathways.
It employs multiple methodologies, including forecasting and decision support, to analyze organizational decisions in broad settings where the uncertainty is high, the risk is complex, and the implications of such decisions .
Reality: Decision making always involves uncertainty Even the simplest decisions carry some level of uncertainty. In choosing a cup of coffee, there will be at least the possibility that the coffee doesn't taste good, is not hot, or will not provide the usual pleasurable feeling.
Decision theory (or the theory of choice) is the study of the reasoning underlying an agent's choices. Decision theory can be broken into two branches: normative decision theory, which gives advice on how to make the best decisions, given a set of uncertain beliefs and a set of values; and descriptive decision theory, which analyzes how existing, possibly irrational agents actually make decisions.
Making Decisions Under Uncertainty Or, How to Decide with Incomplete Data “In any moment of decision, the best thing you can do is the right thing.
The worst thing you can do is nothing.”.