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Advances in the introduction of fluctuating renewable energies, such as photovoltaics (PV), have caused power-system destabilization. However, stability can be improved if consumers change the way they use power, moving to time slots when the PV output in an area is high. In large cities in developed countries, where the types of distributed energy resources are varied, demand side management (DSM) in which consumers share power supplies and adjust the demand has received considerable attention. Under effective DSM that uses the latest information and communication technology to maximize the use of renewable energy, we believe that sparing use of appliances is not the only solution to address global warming. If behavioral change shifts the use of domestic appliances from one time slot to other time slots, we do not have to abandon the use of these appliances. The aim of this study is to determine the possibility of such behavioral changes in people in order to provide basic information for operating an effective DSM. To that end, we conducted a questionnaire-based survey of 10,000 households in Japan. We investigated the proportion of people responding to a request for a demand response (DR) under the given presented reward in time slots when DSM by DR is required. We also analyzed the factors influencing people’s response to a request for a DR. Furthermore, based on the rewards likely to be achieved in the adjustable power market, we estimated how much adjustable power would be realized.

As algorithms are increasingly applied to screen applicants for high-stakes decisions in employment, lending, and other domains, concerns have been raised about the effects of algorithmic monoculture, in which many decision-makers all rely on the same algorithm. This concern invokes analogies to agriculture, where a monocultural system runs the risk of severe harm from unexpected shocks. Here, we show that the dangers of algorithmic monoculture run much deeper, in that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate for any one agent in isolation, can reduce the overall quality of the decisions being made by the full collection of agents. Unexpected shocks are therefore not needed to expose the risks of monoculture; it can hurt accuracy even under “normal” operations and even for algorithms that are more accurate when used by only a single decision-maker. Our results rely on minimal assumptions and involve the development of a probabilistic framework for analyzing systems that use multiple noisy estimates of a set of alternatives.

In this research, we show how modeling shippers' responses to congested freight transportation on an important segment of the Upper Mississippi River (UMR) inland navigation system strongly influences the measurement of expected economic benefits attributed to a range of congestion mitigation measures. We present a model of the UMR that integrates a shippers' random utility model with a discrete event simulation model of the most congested 100-mile segment of the UMR system. The random utility model recognizes that waterway shippers may opt out of using the UMR in response to increased congestion and instead utilize alternative transport modes or destinations. Incorporating the dynamic response of shippers to changing operating conditions improves existing simulation models by explicitly accounting for the preferences and values of shippers, thereby providing a consistent estimate of the direct economic benefits associated with measures designed to reduce congestion and improve system performance. The major contributions of our research include demonstrating the importance of using models that capture shippers' responses to congestion in freight transportation systems and illustrating a novel methodology for quantifying the direct economic benefits to users of measures to improve transportation on the UMR.

We study from both a theoretical and an empirical perspective how a network of military alliances and enmities affects the intensity of a conflict. The model combines elements from network theory and from the politico-economic theory of conflict. We obtain a closed-form characterization of the Nash equilibrium. Using the equilibrium conditions, we perform an empirical analysis using data on the Second Congo War, a conflict that involves many groups in a complex network of informal alliances and rivalries. The estimates of the fighting externalities are then used to infer the extent to which the conflict intensity can be reduced through (i) dismantling specific fighting groups involved in the conflict; (ii) weapon embargoes; (iii) interventions aimed at pacifying animosity among groups. Finally, with the aid of a random utility model, we study how policy shocks can induce a reshaping of the network structure.

In this paper, we study the relationship between several well known classes of discrete choice models, i.e., the random utility model (RUM), the representative agent model (RAM), and the semiparametric choice model (SCM). Using a welfare-based model as an intermediate, we show that the RAM and the SCM are equivalent. Furthermore, we show that both models as well as the welfare-based model strictly subsume the RUM when there are three or more alternatives, while the four are equivalent when there are only two alternatives. Thus, this paper presents a complete picture of the relationship between these choice models.

Discrete choice models are commonly used by applied statisticians in numerous fields, such as marketing, economics, finance, and operations research. When agents in discrete choice models are assumed to have differing preferences, exact inference is often intractable. Markov chain Monte Carlo techniques make approximate inference possible, but the computational cost is prohibitive on the large datasets now becoming routinely available. Variational methods provide a deterministic alternative for approximation of the posterior distribution. We derive variational procedures for empirical Bayes and fully Bayesian inference in the mixed multinomial logit model of discrete choice. The algorithms require only that we solve a sequence of unconstrained optimization problems, which are shown to be convex. One version of the procedures relies on a new approximation to the variational objective function, based on the multivariate delta method. Extensive simulations, along with an analysis of real-world data, demonstrate that variational methods achieve accuracy competitive with Markov chain Monte Carlo at a small fraction of the computational cost. Thus, variational methods permit inference on datasets that otherwise cannot be analyzed without possibly adverse simplifications of the underlying discrete choice model. Appendices C through F are available as online supplemental materials.

Kitamura and Stoye (2018) recently proposed a nonparametric statistical test for random utility models of consumer behavior. The test is formulated in terms of linear inequality constraints and a quadratic objective function. While the nonparametric test is conceptually appealing, its practical implementation is computationally challenging. In this paper, we develop a column generation approach to operationalize the test. These novel computational tools generate considerable computational gains in practice, which substantially increases the empirical usefulness of Kitamura and Stoye’s statistical test.

Individual-level modeling is an essential requirement for effective deployment of smart urban mobility applications. Mode choice behavior is also a core feature in transportation planning models, which are used for analyzing future policies and sustainable plans such as greenhouse gas emissions reduction plans. Specifically, an agent-based model requires an individual level choice behavior, mode choice being one such example. However, traditional utility-based discrete choice models, such as logit models, are limited to aggregated behavior analysis. This paper develops a model employing a deep neural network structure that is applicable to the travel mode choice problem. This paper uses deep learning algorithms to highlight an individual-level mode choice behavior model, which leads us to take into account the inherent characteristics of choice models that all individuals have different choice options, an aspect not considered in the neural network models of the past that have led to poorer performance. Comparative analysis with existing behavior models indicates that the proposed model outperforms traditional discrete choice models in terms of prediction accuracy for both individual and aggregated behavior.

We provide a tractable characterization of the sharp identification region of the parameter vector θ in a broad class of incomplete econometric models. Models in this class have set-valued predictions that yield a convex set of conditional or unconditional moments for the observable model variables. In short, we call these models with convex moment predictions. Examples include static, simultaneous-move finite games of complete and incomplete information in the presence of multiple equilibria; best linear predictors with interval outcome and covariate data; and random utility models of multinomial choice in the presence of interval regressors data. Given a candidate value for 0, we establish that the convex set of moments yielded by the model predictions can be represented as the Aumann expectation of a properly defined random set. The sharp identification region of θ, denoted Θ₁, can then be obtained as the set of minimizers of the distance from a properly specified vector of moments of random variables to this Aumann expectation. Algorithms in convex programming can be exploited to efficiently verify whether a candidate θ is in Θ₁. We use examples analyzed in the literature to illustrate the gains in identification and computational tractability afforded by our method.