Alessandra Cillo

A Quantitative Measurement of Regret Theory

This paper introduces a method to measure regret theory, a popular theory of decision under uncertainty. Regret theory allows for violations of transitivity, and it may seem paradoxical to quantitatively measure an intransitive theory. We adopt the trade-off method and show that it is robust to violations of transitivity. Our method makes no assumptions about the shape of the functions reflecting utility and regret. It can be performed at the individual level, taking account of preference heterogeneity. Our data support the main assumption of regret theory, that people are disproportionately averse to large regrets, even when event-splitting effects are controlled for. The findings are robust: similar results were obtained in two measurements using different stimuli. The data support the reliability of the trade-off method: its measurements could be replicated using different stimuli and were not susceptible to strategic responding.

A Tailor-Made Test of Intransitive Choice

This paper reports a new test of intransitive choice using individual measurements of regret-and similarity-based intransitive models of choice under uncertainty. Our test is tailor-made and uses subject-specific stimuli. Despite these features, we observed only a few intransitivities. A possible explanation for the poor predictive performance of intransitive choice models is that they only allow for interactions between acts. They exclude within-act interactions by retaining the assumption that preferences are separable over states of nature. Prospect theory, which relaxes separability but retains transitivity, predicted choices better. Our data suggest that descriptively realistic models must allow for within-act interactions but may retain transitivity.

Mean-Risk Analysis with Enhanced Behavioral Content

We study a mean-risk model derived from a behavioral theory of Disappointment with multiple reference points. One distinguishing feature of the risk measure is that it is based on mutual deviations of outcomes, not deviations from a specific target. We prove necessary and sufficient conditions for strict first and second order stochastic dominance, and show that the model is, in addition, a Convex Risk Measure. The model allows for richer, and behaviorally more plausible, risk preference patterns than competing models with equal degrees of freedom, including Expected Utility (EU), Mean–Variance (M-V), Mean-Gini (M-G), and models based on non-additive probability weighting, such as Dual Theory (DT). In asset allocation, the model allows a decision-maker to abstain from diversifying in a positive expected value risky asset if its performance does not meet a certain threshold, and gradually invest beyond this threshold, which appears more acceptable than the extreme solutions provided by either EU and M-V (always diversify) or DT and M-G (always plunge). In asset trading, the model provides no-trade intervals, like DT and M-G, in some, but not all, situations. An illustrative application to portfolio selection is presented. The model can provide an improved criterion for mean-risk analysis by injecting a new level of behavioral realism and flexibility, while maintaining key normative properties.

Deciding with Thresholds: Importance Measures and Value of Information

Risk-informed decision making is often accompanied by the specification of an acceptable level of risk. Such target level is compared against the value of a risk metric, usually computed through a probabilistic safety assessment model, to decide about the acceptability of a given design, the launch of a space mission, etc. Importance measures complement the decision process with information about the risk/safety significance of events. However, importance measures do not tell us whether the occurrence of an event can change the overarching decision. By linking value of information and importance measures for probabilistic risk assessment models, this work obtains a value-of-information-based importance measure that brings together the risk metric, risk importance measures, and the risk threshold in one expression. The new importance measure does not impose additional computational burden because it can be calculated from our knowledge of the risk achievement and risk reduction worth, and complements the insights delivered by these importance measures. Several properties are discussed, including the joint decision worth of basic event groups. The application to the large loss of coolant accident sequence of the Advanced Test Reactor helps us in illustrating the risk analysis insights.

Elicitation of multiattribute value functions through high dimensional model representations: Monotonicity and interactions

This work addresses the early phases of the elicitation of multiattribute value functions proposing a practical method for assessing interactions and monotonicity. We exploit the link between multiattribute value functions and the theory of high dimensional model representations. The resulting elicitation method does not state any a-priori assumption on an individual’s preference structure. We test the approach via an experiment in a riskless context in which subjects are asked to evaluate mobile phone packages that differ on three attributes. Keywords: Multiattribute Utility Theory; High Dimensional Model Representations; Value Function Elicitation; Sparse Grid Interpolation.

On the Relationship between Safety and Decision Significance: Relationship between Safety and Decision Significance

Risk analysts are often concerned with identifying key safety drivers, that is, the systems, structures, and components (SSCs) that matter the most to safety. SSCs importance is assessed both in the design phase (i.e., before a system is built) and in the implementation phase (i.e., when the system has been built) using the same importance measures. However, in a design phase, it would be necessary to appreciate whether the failure/success of a given SSC can cause the overall decision to change from accept to reject (decision significance). This work addresses the search for the conditions under which SSCs that are safety significant are also decision significant. To address this issue, the work proposes the notion of a θ-importance measure. We study in detail the relationships among risk importance measures to determine which properties guarantee that the ranking of SSCs does not change before and after the decision is made. An application to a probabilistic safety assessment model developed at NASA illustrates the risk management implications of our work.

Between Cash, Deposit And Bitcoin: Would We Like A Central Bank Digital Currency? Money Demand And Experimental Economics

The aim of this paper is to analyse the demand of a central bank digital currency (CBDC). Using a financial portfolio approach and assuming that individual preferences and policy votes are consistent, we identify the drivers of the political consensus in favour or against such as new currency. Given three different properties of a currency – where the first two are the standard functions of medium of exchange and store of value and the third one is the less explored function of store of information – and three different existing moneys – paper currency, banking currency and cryptocurrency – if the individuals are rational but at the same time can be affected by behavioural biases – loss aversion - three different groups of individuals – respectively lovers, neutrals and haters – emerge respect to the CBDC option. Given the alternative opportunity costs of the different currencies, the CBDC issuing is more likely to occur the more the individuals likes to use a legal tender, and/or are indifferent respect to anonymity; at the same time, the probability of the CBDC introduction increases if a return can be paid on it, and/or its implementation can guarantee at least the counterparty anonymity.

Beyond Bitcoin and Cash: Do We Would Like a Central Bank Digital Currency? A Financial and Political Economics Approach

The aim of this paper is to offer a theoretical primer in order to analyse the demand of a central bank digital currency (CBDC). Using a financial portfolio approach and assuming that individual preferences and policy votes are consistent, we identify the drivers of the political consensus in favour or against such as new currency. Given three different properties of a currency – where the first two are the standard functions of medium of exchange and store of value and the third one is the less explored function of store of information – and three different existing moneys – paper currency, banking currency and cryptocurrency – if the individuals are rational but at the same time can be affected by behavioural biases – loss aversion - three different groups of individuals – respectively lovers, neutrals and haters – emerge respect to the CBDC option. Given the alternative opportunity costs of the different currencies, the CBDC issuing is more likely to occur the more the individuals likes to use a legal tender, and/or are indifferent respect to anonymity; at the same time, the probability of the CBDC introduction increases if a return can be paid on it, and/or its implementation can guarantee at least the counterparty anonymity.