Vicarious Liability Under a Strict Liability Rule

In this article, we extend the model of Newman, H., and D. Wright. 1990. “Strict Liability in a Principal-Agent Model.” International Review of Law and Economics 10: 219–231 and strengthens their result that the strict liability can attain social optimum in a principal-agent relation to the situation in which the court appreciates any contractual terms regarding apportionment of damages between an employer and an employee under vicarious liability rule. Our model also generalizes and extends vicarious liability to the negligence-based liability rule.

The game theory applied to the Covid-19 pandemic

The classification of COVID-19 as a pandemic by the World Health Organization (WHO), substantiated a global crisis in public and economic health, exposing failures of governments and markets in terms of the ability to act in a corrective, preventive and, above all, predictive manner, given the appearance of exogenous factors. One of the visible consequences of the pandemic is the polarization between Economy and Health in the countries, creating a competitive environment that resembles a duopoly where each player ends up acting and making their decisions according to what the other does. This article considers this scenario by quantitatively evaluating economic results that are possible to be achieved when in a negotiation essay between ‘Economics’ and ‘Health’, using the economic theory of games. The discussion developed points out to the existence of an “optimal strategy” for both the ‘Economy’ and ‘Health’ player, capable of maximizing the expected payoff for the population. From the application of the Pareto Equilibrium combined with the Coase Theorem, there is an opportunity to eliminate market and government failures with the achievement of a ‘Social Optimum’ throughout this and eventual future pandemics.

Behavioral Economics and Court Decision-Making

The economic analysis of law assumes that court decisions are key to incentivizing people and maximizing social welfare. This article reviews the behavioral literature on court decision making, and highlights numerous heuristics and biases that impact judges and jurors and cause them to make decisions that diverge from the social optimum. In light of this review, the article analyzes some of the institutional features of the court system that may help minimize the costs of biased decisions in the courts.

Optimal Population Policy with Health Care and Lethal Pollution

Optimal population policy is examined in the following setup. Families invest in capital, spend on health care and determine their number of children. Firms produce output from labor, capital and pollutants. Pollution increases, but private and public health care decrease mortality dynamically, with lags. Our main findings are the following. A marginal increase in public health care improves welfare as long as it diminishes the mortality rate more than that in private health care. The government can decentralize the social optimum by a parental tax on newborns and a Pigouvian tax on pollutants. Private health care should not be taxed.

Dynamic Risked Equilibrium

We study a competitive partial equilibrium in markets where risk-averse agents solve multistage stochastic optimization problems formulated in scenario trees. The agents trade a commodity that is produced from an uncertain supply of resources. Both resources and the commodity can be stored for later consumption. Several examples of a multistage risked equilibrium are outlined, including aspects of battery and hydroelectric storage in electricity markets, distributed ownership of competing technologies relying on shared resources, and aspects of water control and pricing. The agents are assumed to have nested coherent risk measures based on one-step risk measures with polyhedral risk sets that have a nonempty intersection over agents. Agents can trade risk in a complete market of Arrow-Debreu securities. In this setting, we define a risk-trading competitive market equilibrium and establish two welfare theorems. Competitive equilibrium will yield a social optimum (with a suitably defined social risk measure) when agents have strictly monotone one-step risk measures. Conversely, a social optimum with an appropriately chosen risk measure will yield a risk-trading competitive market equilibrium when all agents have strictly monotone risk measures. The paper also demonstrates versions of these theorems when risk measures are not strictly monotone.

Contracts to Coordinate Healthcare Providers in the Telemedicine Referral System

With the help of telemedicine, healthcare providers can increase patients’ access to high-quality services while reducing the medical expenditure, especially for patients in remote areas. Once advanced care is needed, local patients will first be referred to an online health service and then be referred to the offline hospital if the online healthcare fails. In practice, local community hospitals and the advanced tertiary hospitals generally lack financial incentives to exert costly, but non-reimbursable, effort to avoid poor patient outcomes. Therefore, we build a new model to analyze the interaction between these two service providers, promoting them to exert the right effort by designing payment contracts. Our results show that neither fee-for-service nor bundled payment contracts can achieve the social optimum. Tertiary hospitals always exert less effort than the socially-optimal effort while the community hospital may exert less or more effort depending on the online treatment cost. Then, we propose a performance-based bundled payment contract that can coordinate both hospitals’ decisions to achieve socially optimal outcomes. Finally, we numerically show the impact of the referral service fee and the online treatment cost on the efficiency of these contracts.

The price of anarchy in routing games as a function of the demand

The price of anarchy has become a standard measure of the efficiency of equilibria in games. Most of the literature in this area has focused on establishing worst-case bounds for specific classes of games, such as routing games or more general congestion games. Recently, the price of anarchy in routing games has been studied as a function of the traffic demand, providing asymptotic results in light and heavy traffic. The aim of this paper is to study the price of anarchy in nonatomic routing games in the intermediate region of the demand. To achieve this goal, we begin by establishing some smoothness properties of Wardrop equilibria and social optima for general smooth costs. In the case of affine costs we show that the equilibrium is piecewise linear, with break points at the demand levels at which the set of active paths changes. We prove that the number of such break points is finite, although it can be exponential in the size of the network. Exploiting a scaling law between the equilibrium and the social optimum, we derive a similar behavior for the optimal flows. We then prove that in any interval between break points the price of anarchy is smooth and it is either monotone (decreasing or increasing) over the full interval, or it decreases up to a certain minimum point in the interior of the interval and increases afterwards. We deduce that for affine costs the maximum of the price of anarchy can only occur at the break points. For general costs we provide counterexamples showing that the set of break points is not always finite.

Traffic Control Scheme for Social Optimum Traffic Assignment with Dynamic Route Pricing for Automated Vehicles

In transportation modeling, after defining a road network and its origin-destination (OD) matrix, the next important question is how to assign traffic among OD-pairs. Nowadays, advanced traveler information systems (ATIS) make it possible to realize the user equilibrium solution. Simultaneously, with the advent of the Cooperative Intelligent Transport Systems (C-ITS), it is possible to solve the traffic assignment problem in a system optimum way. As a potential traffic assignment method in the future transportation system for automated cars, the deterministic system optimum (DSO) is modeled and simulated to investigate the potential changes it may bring to the existing traditional traffic system. In this paper, stochastic user equilibrium (SUE) is used to simulate the conventional traffic assignment method. This work concluded that DSO has considerable advantages in reducing trip duration, time loss, waiting time, and departure delay under the same travel demand. What is more, the SUE traffic assignment has a more dispersed vehicle density distribution. Moreover, DSO traffic assignment helps the maximum vehicle density of each alternative path arrive almost simultaneously. Furthermore, DSO can significantly reduce or avoid the occurrence of excessive congestion.

Knowledge Accumulation, Privacy, and Growth in a Data Economy

We build an endogenous growth model with consumer-generated data as a new key factor for knowledge accumulation. Consumers balance between providing data for profit and potential privacy infringement. Intermediate good producers use data to innovate and contribute to the final good production, which fuels economic growth. Data are dynamically nonrival with flexible ownership while their production is endogenous and policy-dependent. Although a decentralized economy can grow at the same rate (but are at different levels) as the social optimum on the Balanced Growth Path, the R&D sector underemploys labor and overuses data—an inefficiency mitigated by subsidizing innovators instead of direct data regulation. As a data economy emerges and matures, consumers’ data provision endogenously declines after a transitional acceleration, allaying long-run privacy concerns but portending initial growth traps that call for interventions. This paper was accepted by Kay Giesecke, finance.

Slugging: Casual Carpooling for Urban Transit

Problem definition: “Slugging,” or casual carpooling, refers to the commuting practice of drivers picking up passengers at designated locations and offering them a free ride in order to qualify for high-occupancy vehicle (HOV) lanes. Academic/practical relevance: It is estimated that tens of thousands of daily commuters rely on slugging to go to work in major U.S. cities. As drivers save commute time and passengers ride for free, slugging can be a promising Smart Mobility solution. However, little is known about the welfare, policy, and environmental implications of slugging. Methodology: We develop a stylized model that captures the essence of slugging. We characterize commuters’ equilibrium behavior in the model. Results: We find that slugging indeed makes commuters better off. However, the widely observed free-ride tradition is socially suboptimal. As compared with the social optimum, commuters always underslug in the free-slugging equilibrium when highway travel time is insensitive to slugging activities but may overslug otherwise. The socially optimal outcome can be achieved by allowing pecuniary exchanges between drivers and passengers. Interestingly, passengers may be better off if they pay for a ride than if they do not under free slugging. We also find that although policy initiatives to expand highway capacity or improve public transportation always increase social welfare in the absence of slugging, they may reduce social welfare in areas where free slugging is a major commuting choice. Nevertheless, these unintended consequences would be mitigated by the introduction of pecuniary exchanges. Finally, contrary to conventional wisdom, slugging as a form of carpooling can result in more cars on the road and thus, more carbon emissions. Managerial implications: Our results call upon the slugging community to rethink the free-ride practice. We also caution that slugging benefits commuters possibly to the detriment of the environment.