This paper presents a fundamental #mathematical duality linking utility transforms and #probability distortions, which are vital in #decisionmaking under #risk. It reveals that these concepts are characterized by commutation, allowing for simple axiomatization with just one property. Additionally, rank-dependent utility transforms are further characterized under monotonicity conditions.
The essential role of #ai in #banking holds promise for efficiency, but faces challenges like the opaque "black box" issue, hindering #fairness and #transparency in #decisionmaking #algorithms. Substituting AI with Explainable AI (#xai) can mitigate this problem, ensuring #accountability and #ethical standards. Research on XAI in finance is extensive but often limited to specific cases like #frauddetection and credit #riskassessment.
The submission suggests strategies for regulating #ai in #australia, including examining the rate of take-up of #automated #decisionmaking systems, and regulating specific applications of underlying AI technologies. It also suggests altering the definition of AI, creating a set of guiding principles, and adopting a #risk-based approach to #regulation.
This study examines interpersonal heterogeneity in #risk attitudes in #decisionmaking experiments. The use of #bayesian and classical methods for estimating the hierarchical model has sparked debate. Both approaches use the population distribution of risk attitudes to identify individual-specific risk attitudes. Comparing existing experimental data, both methods yield similar conclusions about risk attitudes.
The paper discusses the importance of #riskinformed #decisionmaking -and the use of #riskassessment to support decisions. It highlights the need for a more dynamic approach to #riskmanagement, which takes into account #uncertainty, changes in systems, phenomena, or values that could alter the underlying premises of the initial risk assessment.
"Bayesian estimates from experimental data can be influenced by highly diffuse or "uninformative" priors. This paper discusses how practitioners can use their own expertise to critique and select a prior that (i) incorporates our knowledge as experts in the field, and (ii) achieves favorable sampling properties. I demonstrate these techniques using data from eleven experiments of decision-making under risk, and discuss some implications of the findings."
"We... apply two stochastic orders to some classic decision problems in economics and finance including a portfolio problem, two insurance problems, and four management decision problems and present a simple sufficient condition for monotone comparative statics of changes in risk under risk aversion."