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Research Summary: We develop a behavioral theory of real options that relaxes the informational and behavioral assumptions underlying applications of financial options theory to real assets. To do so, we augment real option theory's focus on uncertain future asset values (prospective uncertainty) with feedback learning theory that considers uncertain current asset values (contemporaneous uncertainty). This enables us to incorporate behavioral bias in the feedback learning process underlying the option execution/termination decision. The resulting computational model suggests that firms that inappropriately account for contemporaneous uncertainty and are subject to learning biases may experience substantial downside risk in undertaking real options. Moreover, contrary to the standard option result, greater uncertainty may decrease option value, making commitment to an investment path more effective than remaining flexible. Managerial Summary: Executives recognize the need to make uncertain investments to grow their business while mitigating downside risk. The analogy between financial options and real corporate investments provides an appealing method to consider the practical challenge of such investment decisions. Unfortunately, the \"real options\" analogy seems to break down in practice. We identify how a second form of uncertainty confounds real options intuition, leading managers to overestimate the value of uncertain investments. We present a behavioral real options model that accounts for both forms of uncertainty and suggest how uncertainty interacts with behavioral bias in the option execution/termination decision. Our model facilitates assessment of the conditions under which investments in uncertain opportunities are usefully considered as real options, and provides a means to evaluate their attractiveness.

Animals often make decisions without perfect knowledge of environmental parameters like the quality of an encountered food patch or a potential mate. Theoreticians often assume animals make such decisions using a Bayesian updating process that combines prior information about the frequency distribution of resources in the environment with sample information from an encountered resource; such a process leads to decisions that maximize fitness, given the available information. I examine three aspects of empirical work that shed light on the idea that animals can make such decisions in a Bayesian‐like manner. First, many animals are sensitive to variance differences in behavioral options, one metric used to characterize frequency distributions. Second, several species use information about the relative frequency of preferred versus nonpreferred items in different populations to make probabilistic inferences about samples taken from populations in a manner that results in maximizing the likelihood of obtaining a preferred reward. Third, the predictions of Bayesian models often match the behavior of individuals in two main approaches. One approach compares behavior to models that make different assumptions about how individuals estimate the quality of an environmental parameter. The patch exploitation behavior of nine species of birds and mammals has matched the predictions of Bayesian models. The other approach compares the behavior of individuals who learn, through experience, different frequency distributions of resources in their environment. The behavior of three bird species and bumblebees exploiting food patches and fruit flies selecting mates is influenced by their experience learning different frequency distributions of food and mates, respectively, in ways consistent with Bayesian models. These studies lend support to the idea that animals may combine prior and sample information in a Bayesian‐like manner to make decisions under uncertainty, but additional work on a greater diversity of species is required to better understand the generality of this ability. I examine empirical work that sheds light on the idea that animals can make decisions in a Bayesian‐like manner. I show evidence that animals are sensitive to variation in reward options and can make probabilistic inferences, two foundations of Bayesian‐like estimation. I also show that Bayesian models often predict the behavior of individuals in different environmental contexts.

Multiarmed bandit (MAB) is a classic model for capturing the exploration–exploitation trade-off inherent in many sequential decision-making problems. The classic MAB framework, however, only allows “local” constraints on decisions and “sum of rewards” as objective. In many real-world applications, there are multiple complex constraints on resources that are consumed during the entire decision process, and performance may be evaluated through nonlinear utility functions on aggregate rewards. This article presents a new MAB framework that allows such “global” convex constraints and concave objective functions along with new algorithmic techniques with provably near-optimal performance bounds. The authors discuss applications in several domains, such as network revenue management, crowdsourcing, and pay-per-click advertising, which benefit from the new more general framework by admitting richer models and more efficient risk-averse solutions. We consider a very general model for managing the exploration–exploitation trade-off, which allows global convex constraints and concave objective on the aggregate decisions over time in addition to the customary limitation on the time horizon. This model provides a natural framework to study many sequential decision-making problems with long-term convex constraints and concave utility and subsumes the classic multiarmed bandit (MAB) model and the bandits with knapsacks problem as special cases. We demonstrate that a natural extension of the upper confidence bound family of algorithms for MAB provides a polynomial time algorithm with near-optimal regret guarantees for this substantially more general model. We also provide computationally more efficient algorithms by establishing interesting connections between this problem and other well-studied problems/algorithms, such as the Blackwell approachability problem, online convex optimization, and the Frank–Wolfe technique for convex optimization. We give several concrete examples of applications, particularly in risk-sensitive revenue management under unknown demand distributions, in which this more general bandit model of sequential decision making allows for richer formulations and more efficient solutions of the problem.

For three decades, advocates for climate change policy have simultaneously emphasized the urgency of taking ambitious actions to mitigate greenhouse gas (GHG) emissions and provided false reassurances of the feasibility of doing so. The policy prescription has relied almost exclusively on a single approach: reduce emissions of carbon dioxide (CO2) and other GHGs. Since 1990, global CO2 emissions have increased 60%, atmospheric CO2 concentrations have raced past 400 ppm, and temperatures increased at an accelerating rate. The one‐prong strategy has not worked. After reviewing emission mitigation's poor performance and low‐probability of delivering on long‐term climate goals, we advance a three‐pronged strategy for mitigating climate change risks: adding adaptation and amelioration—through solar radiation management (SRM)—to the emission mitigation approach. We highlight SRM's potential, at dramatically lower cost than emission mitigation, to play a key role in offsetting warming. We address the moral hazard reservation held by environmental advocates—that SRM would diminish emission mitigation incentives—and posit that SRM deployment might even serve as an “awful action alert” that galvanizes more ambitious emission mitigation. We conclude by emphasizing the value of an iterative act‐learn‐act policy framework that engages all three prongs for limiting climate change damages.

We develop a tractable method to estimate multiple prior models of decisionmaking under ambiguity. In a representative sample of the U.S. population, we measure ambiguity attitudes in the gain and loss domains. We find that ambiguity aversion is common for uncertain events of moderate to high likelihood involving gains, but ambiguity seeking prevails for low likelihoods and for losses. We show that choices made under ambiguity in the gain domain are best explained by the α-MaxMin model, with one parameter measuring ambiguity aversion (ambiguity preferences) and a second parameter quantifying the perceived degree of ambiguity (perceptions about ambiguity). The ambiguity aversion parameter a is constant and prior probability sets are asymmetric for low and high likelihood events. The data reject several other models, such as MaxMin and MaxMax, as well as symmetric probability intervals. Ambiguity aversion and the perceived degree of ambiguity are both higher for men and for the college-educated. Ambiguity aversion (but not perceived ambiguity) is also positively related to risk aversion. In the loss domain, we find evidence of reflection, implying that ambiguity aversion for gains tends to reverse into ambiguity seeking for losses. Our model's estimates for preferences and perceptions about ambiguity can be used to analyze the economic and financial implications of such preferences.

Ensemble forecasting has become popular in weather prediction to reflect the uncertainty about high‐dimensional, nonlinear systems with extreme sensitivity to initial conditions. By means of small strategical perturbations of the initial conditions, sometimes accompanied with stochastic parameterisation schemes of the atmosphere–ocean dynamical equations, ensemble forecasting aims at sampling possible future scenario and ideally at interpreting them in a Monte‐Carlo‐like approximation. Traditional probabilistic interpretations of ensemble forecasts do not take epistemic uncertainty into account nor the fact that ensemble predictions cannot always be interpreted in a density‐based manner due to the strongly nonlinear dynamics of the atmospheric system. As a result, probabilistic predictions are not always reliable, especially in the case of extreme events. In this work, we investigate whether relying on possibility theory, an uncertainty theory derived from fuzzy set theory and connected to imprecise probabilities, can circumvent these limitations. We show how it can be used to compute confidence intervals with guaranteed reliability, when a classical probabilistic postprocessing technique fails to do so in the case of extreme events. We illustrate our approach with an imperfect version of the Lorenz 96 model and demonstrate that it is promising for risk‐averse decision‐making. Traditional probabilistic interpretations of ensemble weather forecasts do not take into account epistemic uncertainty nor the fact that in some cases (e.g., extreme events), ensemble predictions cannot be interpreted in a density‐based manner. We investigate the potential of possibility theory to circumvent these limitations. In particular, we show that a possibilistic interpretation provides confidence intervals with guaranteed reliability, even at large lead times or for extreme events, highlighting its potential for risk‐averse decision‐making.

This paper proposes a predictive dispatch model to manage energy flexibility in the domestic energy system. Electric Vehicles (EV), batteries and shiftable loads are devices that provide energy flexibility in the proposed system. The proposed energy management problem consists of two stages: day-ahead and real time. A hybrid method is defined for the first time in this paper to model the uncertainty of the PV power generation based on its power prediction. In the day-ahead stage, the uncertainty is modeled by interval bands. On the other hand, the uncertainty of PV power generation is modeled through a stochastic scenario-based method in the real-time stage. The performance of the proposed hybrid Interval-Stochastic (InterStoch) method is compared with the Modified Stochastic Predicted Band (MSPB) method. Moreover, the impacts of energy flexibility and the demand response program on the expected profit and transacted electrical energy of the system are assessed in the case study presented in this paper.

Empirical studies of ambiguity aversion mostly use artificial events such as Ellsberg urns to control for unknown probability beliefs. The present study measures ambiguity attitudes using real-world events in a large sample of investors. We elicit ambiguity aversion and perceived ambiguity for a familiar company stock, a local stock index, a foreign stock index, and Bitcoin. Measurement reliability is higher than for artificial sources in previous studies. Ambiguity aversion is highly correlated for different assets, while perceived ambiguity varies more between assets. Further, we show that ambiguity attitudes are related to actual investment choices.

The field of international business is fundamentally concerned with the implications of managerial actions that affect multinational risk and performance outcomes. While portfolio diversification and real options theory are often used to describe the outcomes of multinational investment, existing work often confuses the actions and predictions proposed by these theories. This is concerning, as the two theories emphasize different causal mechanisms, managerial actions, and conceptions of risk and performance. Whereas portfolio theory argues that passive management affects symmetric outcomes, such as variance in returns by attaining a well-diversified portfolio, real options theory posits that managers actively shift subsidiary resources to affect asymmetric outcomes, such as upside potential or downside risk by monitoring and responding to environmental changes affecting the portfolio. This paper disentangles these two theories by focusing on unique predictions from real options theory – that geographic dispersion of MNE activities is associated with asymmetric outcomes, that this association is contingent on management being aware of real options logic, and that these effects are moderated by the degree of market uncertainty. Our findings confirm these predictions and suggest differences in the types of managerial strategies and actions required to effectively implement these distinct theories of the MNE.

It is natural for humans to judge the outcome of a decision under uncertainty as a percentage of an ex-post optimal performance. We propose a robust decision-making framework based on a relative performance index. It is shown that if the decision maker’s preferences satisfy quasisupermodularity, single-crossing, and a nondecreasing log-differences property, the worst-case relative performance index can be represented as the lower envelope of two extremal performance ratios. The latter is used to characterize the agent’s optimal robust decision, which has implications both computationally and for obtaining closed-form solutions. We illustrate our results in an application which compares the performance of relative robustness to solutions that optimize worst-case payoffs, maximum absolute regret, and expected payoffs under a Laplacian prior.