Decision-Making Under Indeterminacy

Decisions are made under uncertainty when there are distinct outcomes of a given action, and one is uncertain to which the act will lead. Decisions are made under indeterminacy when there are distinct outcomes of a given action, and it is indeterminate to which the act will lead. This paper develops a theory of (synchronic and diachronic) decision-making under indeterminacy that portrays the rational response to such situations as inconstant. Rational agents have to capriciously and randomly choose how to resolve the indeterminacy relevant to a given choice-situation, but such capricious choices once made constrain how they will choose in the future. The account is illustrated by the case of self-interested action in situations where it is indeterminate whether you yourself will survive to benefit or suffer the consequences. The conclusion emphasizes some distinctive anti-hedging predictions of the account

Decision-Making Under Uncertainty: Beyond Probabilities

This position paper reflects on the state-of-the-art in decision-making under uncertainty. A classical assumption is that probabilities can sufficiently capture all uncertainty in a system. In this paper, the focus is on the uncertainty that goes beyond this classical interpretation, particularly by employing a clear distinction between aleatoric and epistemic uncertainty. The paper features an overview of Markov decision processes (MDPs) and extensions to account for partial observability and adversarial behavior. These models sufficiently capture aleatoric uncertainty but fail to account for epistemic uncertainty robustly. Consequently, we present a thorough overview of so-called uncertainty models that exhibit uncertainty in a more robust interpretation. We show several solution techniques for both discrete and continuous models, ranging from formal verification, over control-based abstractions, to reinforcement learning. As an integral part of this paper, we list and discuss several key challenges that arise when dealing with rich types of uncertainty in a model-based fashion

A smooth model of decision making under ambiguity.

We propose and axiomatize a new model of preferences that achieves a separation between ambiguity, identified as a characteristic of the decision maker's subjective information, and ambiguity attitude, a characteristic of the decision maker's tastes.Ambiguity; Uncertainty; Knightian Uncertainty; Ambiguity Aversion; Uncertainty Aversion; Ellsberg Paradox

Decision theory under uncertainty

We review recent advances in the field of decision making under uncertainty or ambiguity.Ambiguity ; ambiguity aversion ; uncertainty ; decision

Water Resources Decision Making Under Uncertainty

Uncertainty is in part about variability in relation to the physical characteristics of water resources systems. But uncertainty is also about ambiguity (Simonovic, 2009). Both variability and ambiguity are associated with a lack of clarity because of the behaviour of all system components, a lack of data, a lack of detail, a lack of structure to consider water resources management problems, working and framing assumptions being used to consider the problems, known and unknown sources of bias, and ignorance about how much effort it is worth expending to clarify the management situation. Climate change, addressed in this research project (CFCAS, 2008), is another important source of uncertainty that contributes to the variability in the input variables for water resources management. This report presents a set of examples that illustrate (a) probabilistic and (b) fuzzy set approaches for solving various water resources management problems. The main goal of this report is to demonstrate how information provided to water resources decision makers can be improved by using the tools that incorporate risk and uncertainty. The uncertainty associated with water resources decision making problems is quantified using probabilistic and fuzzy set approaches. A set of selected examples are presented to illustrate the application of probabilistic and fuzzy simulation, optimization, and multi-objective analysis to water resources design, planning and operations. Selected examples include dike design, sewer pipe design, optimal operations of a single purpose reservoir, and planning of a multi-purpose reservoir system. Demonstrated probabilistic and fuzzy tools can be easily adapted to many other water resources decision making problems.https://ir.lib.uwo.ca/wrrr/1035/thumbnail.jp

Behavioural Financial Decision Making Under Uncertainty

Ever since von Neumann and Morgenstern published the axiomisation of Expected Utility Theory, there have been a considerable amount of ob- servations appeared in the literature violating the expected utility theory. To make decisions under uncertainty, people generally separate possible outcomes into gains and losses. They are risk averse for gains but risk seeking for losses with very large probabilities; risk averse for losses but risk seeking for gains with very small probabilities. To accommodate these characteristics, Prospect Theory and its improvement Cumulative Prospect Theory were developed in order to formulate people's behaviours under uncertainty in a descriptive and normative way. As such, values are assigned to gains and losses and probabilities are replaced by probability weighting functions. The CPT models built in this project are based on the power value function and the compound invariant form of probability weighting function. The models are calibrated with the data from Hong Kong Mark Six lottery market. The parameters in the models are esti- mated, hence to examine properties of the models and give an insights into how they fit the real life situation. In the first approach, the parameter in the value function is fixed, but the plots of the estimated probability weighting function do not give sensible explanations of lottery player's behaviours. In the second approach, the parameters in value function and weighting function are both estimated from the data to give an optimal fitting of the model