Dynamic Variational Preferences

We introduce and axiomatize dynamic variational preferences, the dynamic version of the variational preferences we axiomatized in [21], which generalize the multiple priors preferences of Gilboa and Schmeidler [9], and include the Multiplier Preferences inspired by robust control and first used in macroeconomics by Hansen and Sargent (see [11]), as well as the classic Mean Variance Preferences of Markovitz and Tobin. We provide a condition that makes dynamic variational preferences time consistent, and their representation recursive. This gives them the analytical tractability needed in macroeconomic and financial applications. A corollary of our results is that Multiplier Preferences are time consistent, but Mean Variance Preferences are not.Ambiguity Aversion; Model Uncertainty; Recursive Utility; Robust Control; Time Consistency

Optimal Stopping with Dynamic Variational Preferences

We consider optimal stopping problems in uncertain environments for an agent assessing utility by virtue of dynamic variational preferences or, equivalently, assessing risk by dynamic convex risk measures. The solution is achieved by generalizing the approach in terms of multiple priors introducing the concept of variational supermartingales and an accompanying theory. To illustrate results, we consider prominent examples: dynamic entropic risk measures and a dynamic version of generalized average value at risk.optimal Stopping, Uncertainty, Dynamic Variational Preferences, Dynamic Convex Risk Measures, Dynamic Penalty, Time-Consistency, Entropic Risk, Average Value at Risk

Merging of Opinions under Uncertainty

We consider long-run behavior of agents assessing risk in terms of dynamic convex risk measures or, equivalently, utility in terms of dynamic variational preferences in an uncertain setting. By virtue of a robust representation, we show that all uncertainty is revealed in the limit and agents behave as expected utility maximizer under the true underlying distribution regardless of their initial risk anticipation. In particular, risk assessments of distinct agents converge. This result is a generalization of the fundamental Blackwell-Dubins Theorem, cp. [Blackwell & Dubins, 62], to convex risk. We furthermore show the result to hold in a non-time-consistent environment.Dynamic Convex Risk Measures, Multiple Priors, Uncertainty, Robust Representation, Time-Consistency, Blackwell-Dubins

Merging of Opinions under Uncertainty

We consider long-run behavior of agents assessing risk in terms of dynamic convex risk measures or, equivalently, utility in terms of dynamic variational preferences in an uncertain setting. By virtue of a robust representation, we show that all uncertainty is revealed in the limit and agents behave as expected utility maximizer under the true underlying distribution regardless of their initial risk anticipation. In particular, risk assessments of distinct agents converge. This result is a generalization of the fundamental Blackwell-Dubins Theorem, cp. [Blackwell & Dubins, 62], to convex risk. We furthermore show the result to hold in a non -time-consistent environment.Dynamic Convex Risk Measures, Multiple Priors, Uncertainty, Robust Representation, Time-Consistency, Blackwell-Dubins.

On Dynamic Coherent and Convex Risk Measures : Risk Optimal Behavior and Information Gains

We consider tangible economic problems for agents assessing risk by virtue of dynamic coherent and convex risk measures or, equivalently, utility in terms of dynamic multiple priors and dynamic variational preferences in an uncertain environment. Solutions to the Best-Choice problem for a risky number of applicants are well-known. In Chapter 2, we set up a model with an ambiguous number of applicants when the agent assess utility with multiple prior preferences. We achieve a solution by virtue of multiple prior Snell envelopes for a model based on so called assessments. The main result enhances us with conditions for the ambiguous problem to possess finitely many stopping islands. In Chapter 3 we consider general optimal stopping problems for an agent assessing utility by virtue of dynamic variational preferences. Introducing variational supermartingales and an accompanying theory, we obtain optimal solutions for the stopping problem and a minimax result. To illustrate, we consider prominent examples: dynamic entropic risk measures and a dynamic version of generalized average value at risk. In Chapter 4, we tackle the problem how anticipation of risk in an uncertain environment changes when information is gathered in course of time. A constructive approach by virtue of the minimal penalty function for dynamic convex risk measures reveals time-consistency problems. Taking the robust representation of dynamic convex risk measures as given, we show that all uncertainty is revealed in the limit, i.e. agents behave as expected utility maximizers given the true underlying distribution. This result is a generalization of the fundamental Blackwell-Dubins theorem showing coherent as well as convex risk measures to merge in the long run

Social choice of convex risk measures through Arrovian aggregation of variational preferences

It is known that a combination of the Maccheroni-Marinacci-Rustichini (2006) axiomatisation of variational preferences with the Föllmer-Schied (2002,2004) representation theorem for concave monetary utility functionals provides an (individual) decision-theoretic foundation for convex risk measures. The present paper is devoted to collective decision making with regard to convex risk measures and addresses the existence problem for non-dictatorial aggregation functions of convex risk measures - in the guise of variational preferences - satisfying Arrow-type rationality axioms (weak universality, systematicity, Pareto principle). We prove an impossibility result for finite electorates, viz. a variational analogue of Arrow's impossibility theorem. For infinite electorates, the possibility of rational aggregation of variational preferences (i.e. convex risk measures) depends on a uniform continuity condition for the variational preference profiles: We shall prove variational analogues of both Campbell's impossibility theorem and Fishburn's possibility theorem. Methodologically, we adopt the model-theoretic approach to aggregation theory inspired by Lauwers-Van Liedekerke (1995). In an appendix, we apply the Dietrich-List (2010) analysis of logical aggregation based on majority voting to the problem of variational preference aggregation. The fruit is a possibility theorem, but at the cost of considerable and - at least at first sight - rather unnatural restrictions on the domain of the variational preference aggregator.variational preference representation, convex risk measure, multiple priors preferences, Arrow-type preference aggregation, judgment aggregation, abstract aggregation theory, model theory, first-order predicate logic, ultrafilter, ultraproduct

Optimal stopping under ambiguity

We consider optimal stopping problems for ambiguity averse decision makers with multiple priors. In general, backward induction fails. If, however, the class of priors is time-consistent, we establish a generalization of the classical theory of optimal stopping. To this end, we develop first steps of a martingale theory for multiple priors. We define minimax (super)martingales, provide a Doob-Meyer decomposition, and characterize minimax martingales. This allows us to extend the standard backward induction procedure to ambiguous, time-consistent preferences. The value function is the smallest process that is a minimax supermartingale and dominates the payoff process. It is optimal to stop when the current payoff is equal to the value function. Moving on, we study the infinite horizon case. We show that the value process satisfies the same backward recursion (Bellman equation) as in the finite horizon case. The finite horizon solutions converge to the infinite horizon solution. Finally, we characterize completely the set of time-consistent multiple priors in the binomial tree. We solve two classes of examples: the so-called independent and indistinguishable case (the parking problem) and the case of American Options (Cox-Ross-Rubinstein model).optimal stopping, ambiguity, uncertainty aversion

Updating Ambiguity Averse Preferences

Dynamic consistency leads to Bayesian updating under expected utility. We ask what it implies for the updating of more general preferences. In this paper, we charac- terize dynamically consistent update rules for preference models satisfying ambiguity aversion. This characterization extends to regret-based models as well. As an appli- cation of our general result, we characterize dynamically consistent updating for two important models of ambiguity averse preferences: the ambiguity averse smooth am- biguity preferences (Klibanoff, Marinacci and Mukerji [Econometrica 73 2005, pp. 1849-1892]) and the variational preferences (Maccheroni, Marinacci and Rustichini [Econometrica 74 2006, pp. 1447-1498]). The latter includes max-min expected utility (Gilboa and Schmeidler [Journal of Mathematical Economics 18 1989, pp. 141-153]) and the multiplier preferences of Hansen and Sargent [American Economic Review 91(2) 2001, pp. 60-66] as special cases. For smooth ambiguity preferences, we also identify a simple rule that is shown to be the unique dynamically consistent rule among a large class of rules that may be expressed as reweightings of Bayes's rule.Updating, Dynamic Consistency, Ambiguity, Regret, Ellsberg, Bayesian, Consequentialism, Smooth Ambiguity

Temporal Resolution of Uncertainty and Recursive Models of Ambiguity Aversion

Dynamic models of ambiguity aversion are increasingly popular in applied work. This paper shows that there is a strong interdependence in such models between the ambiguity attitude and the preference for the timing of the resolution of uncertainty, as defined by the classic work of Kreps and Porteus (1978). The modeling choices made in the domain of ambiguity aversion influence the set of modeling choices available in the domain of timing attitudes. The main result is that the only model of ambiguity aversion that exhibits indifference to timing is the maxmin expected utility of Gilboa and Schmeidler (1989). This paper examines the structure of the timing nonindifference implied by the other commonly used models of ambiguity aversion. This paper also characterizes the indifference to long-run risk, a notion introduced by Duffie and Epstein (1992). The interdependence of ambiguity and timing that this paper identifies is of interest both conceptually and practically—especially for economists using these models in applications.Economic

Tuned risk aversion as interpretation of non-expected utility preferences

We introduce the notion of Tuned Risk Aversion as a possible interpretation of non-expected utility preferences. It refers to tuning patterns of risk (and ambiguity) aversion to the composition of a lottery (or act) at hand, assuming only an overall ‘budget’ for accumulated risk aversion over its sub-lotteries. This makes the risk aversion level applied to a part intrinsically depending on the whole, in a way that turns out to be in line with frequently observed deviations from the Sure-Thing Principle. This is illustrated by applying the concept to the Allais paradox and to the 50:51 example, related to ambiguity aversion. We give a general justification for applying the method in contexts where the law of one price does not hold, and derive unique updating from a substitution axiom induced by a non-recursive form of consistency. In a third example, we propose a solution to a well-known puzzle on consistency of decision making in the Ellsberg parado