Massimo Marinacci

While models used to evaluate climate change are inherently simplified, and thus imperfect, representations of reality, they remain indispensable tools for quantifying policy impacts and guiding decision-making. How can we leverage these models while accounting for their potential misspecification -the possibility that none of the existing models is adequate? This paper addresses this question by applying recent advances in decision theory to analyze how concerns about model misspecification influence key climate strategies, including the stringency of emissions targets and the extent of climate overshoot. Using a decision-theoretic framework applied to emulators of IPCC-assessed scenarios, we quantify the trade-offs between mitigation costs and climate-induced economic damages. We show that when decision-makers explicitly account for the possibility of model misspecification, they rationally adopt more ambitious climate goals, characterized by stricter carbon budgets and reduced reliance on emissions overshoot. These results underscore the importance of integrating model misspecification concerns into climate policy design, providing a rigorous foundation for precautionary and robust decision-making under deep uncertainty.

We study continuous-time portfolio choice with nonlinear payoffs under smooth ambiguity and Bayesian learning. We develop a general framework for dynamic, non-concave asset allocation that accommodates nonlinear payoffs, broad utility classes, and flexible ambiguity attitudes. Dynamic consistency is obtained by a robust representation that recasts the ambiguity-averse problem as ambiguityneutral with distorted priors. This structure delivers explicit trading rules by combining nonlinear filtering with the martingale approach and nests standard concave and linear-payoff benchmarks. As a leading application, delegated management with convex incentives illustrates that ambiguity aversion shifts beliefs toward adverse states, limits the range of states that would otherwise trigger more aggressive risk taking, and reduces volatility through lower risky exposure.

Monotonicity and recursivity are central assumptions in intertemporal consumption problems under ambiguity. We showthatmonotone recursive preferences admit both a recursive and an ex-ante representation, and that the certainty equivalent functionals associated with these representations are translation invariant. Translation invariance implies that the decision maker’s ambiguity attitudes are constant, in the sense that they do not vary with the level of welfare or utility. Finally, we establish an equation linking the two certainty equivalent functionals that extends the notion of rectangularity for recursive multiple priors, which we call generalized rectangularity. This equation yields a simple, testable condition for dynamic consistency.

Maritime insurance developed in medieval Europe is the ancestor of all forms of insurance that appeared subsequently. We address the question of why modern insurance was first invented in medieval Europe, and neither earlier nor elsewhere. Drawing from insights from the literature on uncertainty aversion, we show that medieval merchants had to bear more frequently natural risks (they traveled longer distances) and new human risks with unknown probabilities (they faced unpredictable attacks by corsairs due to increased political fragmentation and commercial competition in Europe). The increased demand for protection in medieval seaborne trade met the supply of protection by a small group of wealthy merchants with a broad information network who could pool risks and profit from selling protection through a novel business device: the insurance contract. A new market—the market for insurance—was then born. Next, analyzing more than 7,000 insurance contracts redacted by notaries and about 100 court proceedings housed in the archives of Barcelona, Florence, Genoa, Palermo, Prato, and Venice, we study the main features of medieval trade, the type of risks faced by merchants, and the characteristics of insurance contracts and markets from 1340 to 1500.The empirical analysis delivers two main findings. First, risks related to human activities (e.g., attacks by corsairs) seem to have had a relatively greater impact on insurance premia compared to natural risks (proxied by seasonal risks). Second, distance mattered but the route seems to have had a greater impact on insurance premia. Specific routes (e.g., in the Tyrrhenian and the western Mediterranean) were more plagued by human risks, which were harder to avoid for the majority of merchants who did not have a broad information network compared to the few wealthy merchants, who became the key players in selling insurance in the early stages of the development of insurance markets.

The drift diffusion model (DDM) is a prominent account of how people make decisions. Many of these decisions involve comparing two alternatives based on differences of perceived stimulus magnitudes, such as economic values. Here, we propose a consistent estimator for the parameters of a DDM in such cases. This estimator allows us to derive decision thresholds, drift rates, and subjective percepts (i.e., utilities in economic choice) directly from the experimental data. This eliminates the need to measure these values separately or to assume specific functional forms for them. Our method also allows one to predict drift rates for comparisons that did not occur in the dataset. We apply the method to two datasets, one comparing probabilities of earning a fixed reward and one comparing objects of variable reward value. Our analysis indicates that both datasets conform well to the DDM. We find that utilities are linear in probability and slightly convex in reward.

We illustrate the strong implications of recursivity, a standard assumption in dynamic environments, on attitudes toward uncertainty. In intertemporal consumption choice problems, recursivity always implies constant absolute ambiguity aversion (CAAA) when applying the standard dynamic extension of monotonicity. Our analysis also yields a functional equation called “generalized rectangularity”, as it generalizes the standard notion of rectangularity for recursive maxmin preferences to general certainty equivalents. Our results highlight that if uncertainty aversion is modeled as a form of convexity of preferences, recursivity limits us to only recursive variational preferences.

This paper provides a general framework for analyzing self-confirming policies. We study self-confirming equilibria in recurrent decision problems with incomplete information about the true stochastic model. We characterize stationary monetary policies in a linear-quadratic setting.