Costly Offers and the Equilibration Properties of the Multiple Unit Double Auction Under Conditions of Unpredictable Shifts of Demand and Supply

The paper reports on the behavior of markets in which a transactions cost is imposed in the form of a tax on bids and asks that are tendered in the market. That is, in the markets studied communication with the other side of the market was costly. The markets were nonstationary in the sense that market demand and market supply shifted unpredictably each period and the markets were organized by the computerized Multiple Unit Double Auction. The results are as follow. (1) A market equilibration process is observed across the periods of nonstationary markets. (2) The imposition of the cost on offers did not negate the tendency toward market equilibration but the price discovery process was "incomplete" relative to the free offer case. (3) Price equilibration with the offer cost was slower and efficiencies were reduced

Comparing the impact on COVID-19 mortality of self-imposed behavior change and of government regulations across 13 countries.

OBJECTIVE: Countries have adopted different approaches, at different times, to reduce the transmission of coronavirus disease 2019 (COVID-19). Cross-country comparison could indicate the relative efficacy of these approaches. We assess various nonpharmaceutical interventions (NPIs), comparing the effects of voluntary behavior change and of changes enforced via official regulations, by examining their impacts on subsequent death rates. DATA SOURCES: Secondary data on COVID-19 deaths from 13 European countries, over March-May 2020. STUDY DESIGN: We examine two types of NPI: the introduction of government-enforced closure policies and self-imposed alteration of individual behaviors in the period prior to regulations. Our proxy for the latter is Google mobility data, which captures voluntary behavior change when disease salience is sufficiently high. The primary outcome variable is the rate of change in COVID-19 fatalities per day, 16-20 days after interventions take place. Linear multivariate regression analysis is used to evaluate impacts. DATA COLLECTION/EXTRACTION METHODS: publicly available. PRINCIPAL FINDINGS: Voluntarily reduced mobility, occurring prior to government policies, decreases the percent change in deaths per day by 9.2 percentage points (pp) (95% confidence interval [CI] 4.5-14.0 pp). Government closure policies decrease the percent change in deaths per day by 14.0 pp (95% CI 10.8-17.2 pp). Disaggregating government policies, the most beneficial for reducing fatality, are intercity travel restrictions, canceling public events, requiring face masks in some situations, and closing nonessential workplaces. Other sub-components, such as closing schools and imposing stay-at-home rules, show smaller and statistically insignificant impacts. CONCLUSIONS: NPIs have substantially reduced fatalities arising from COVID-19. Importantly, the effect of voluntary behavior change is of the same order of magnitude as government-mandated regulations. These findings, including the substantial variation across dimensions of closure, have implications for the optimal targeted mix of government policies as the pandemic waxes and wanes, especially given the economic and human welfare consequences of strict regulations

Unequal consequences of Covid 19: representative evidence from six countries

Covid-19 and the measures taken to contain it have led to unprecedented constraints on work and leisure activities, across the world. This paper uses nationally representative surveys to document how people of different ages and incomes have been affected in the early phase of the pandemic. The data was collected in six countries (China, South Korea, Japan, Italy, UK, and US) in the third week of April 2020. First, we document changes in job circumstances and social activities. Second, we document self-reported negative and positive consequences of the crisis on well-being. We find that young people have experienced more drastic changes to their life and have been most affected economically and psychologically. There is less of a systematic pattern across income groups. While lower income groups have been more affected economically, higher income groups have experienced more changes in their social life and spending. A large fraction of people of low and high income groups report negative effects on well-being

Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic

Given the role of human behavior in the spread of disease, it is vital to understand what drives people to engage in or refrain from health-related behaviors during a pandemic. This paper examines factors associated with the adoption of self-protective health behaviors, such as social distancing and mask wearing, at the start of the Covid-19 pandemic in the USA. These behaviors not only reduce an individual’s own risk of infection but also limit the spread of disease to others. Despite these dual benefits, universal adoption of these behaviors is not assured. We focus on the role of socioeconomic differences in explaining behavior, relying on data collected in April 2020 during the early stages of the Covid-19 pandemic. The data include information on income, gender and race along with unique variables relevant to the current pandemic, such as work arrangements and housing quality. We find that higher income is associated with larger changes in self-protective behaviors. These gradients are partially explained by the fact that people with less income are more likely to report circumstances that make adopting self-protective behaviors more difficult, such as an inability to tele-work. Both in the USA and elsewhere, policies that assume universal compliance with self-protective measures—or that otherwise do not account for socioeconomic differences in the costs of doing so—are unlikely to be effective or sustainable

Computational ethics

Technological advances are enabling roles for machines that present novel ethical challenges. The study of 'AI ethics' has emerged to confront these challenges, and connects perspectives from philosophy, computer science, law, and economics. Less represented in these interdisciplinary efforts is the perspective of cognitive science. We propose a framework – computational ethics – that specifies how the ethical challenges of AI can be partially addressed by incorporating the study of human moral decision-making. The driver of this framework is a computational version of reflective equilibrium (RE), an approach that seeks coherence between considered judgments and governing principles. The framework has two goals: (i) to inform the engineering of ethical AI systems, and (ii) to characterize human moral judgment and decision-making in computational terms. Working jointly towards these two goals will create the opportunity to integrate diverse research questions, bring together multiple academic communities, uncover new interdisciplinary research topics, and shed light on centuries-old philosophical questions.publishedVersio

Computational ethics

Technological advances are enabling roles for machines that present novel ethical challenges. The study of 'AI ethics' has emerged to confront these challenges, and connects perspectives from philosophy, computer science, law, and economics. Less represented in these interdisciplinary efforts is the perspective of cognitive science. We propose a framework – computational ethics – that specifies how the ethical challenges of AI can be partially addressed by incorporating the study of human moral decision-making. The driver of this framework is a computational version of reflective equilibrium (RE), an approach that seeks coherence between considered judgments and governing principles. The framework has two goals: (i) to inform the engineering of ethical AI systems, and (ii) to characterize human moral judgment and decision-making in computational terms. Working jointly towards these two goals will create the opportunity to integrate diverse research questions, bring together multiple academic communities, uncover new interdisciplinary research topics, and shed light on centuries-old philosophical questions

Bringing game theory back to earth : thinking, feeling, and talking

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 1998.Includes bibliographical references (p. 77-78).by Julian Christopher Jamison.Ph.D

Games with synergistic preferences

In economic situations a player often has preferences regarding not only his or her own outcome but also regarding what happens to fellow players, concerns that are entirely apart from any strategic considerations. While this can be modeled directly by simply writing down a player's final preferences, these are commonly unknown a priori. In many cases it is therefore both helpful and instructive to explicitly model these interactions. This paper, building on a model due to Bergstrom (1989, 1999), presents a simple structure in the context of game theory that incorporates the "synergies" between players. It is powerful enough to cover a wide range of such interactions and model many disparate experimental and empirical results, yet it is straightforward enough to be used in many applied situations where altruism, or a baser motive, is implied.Human behavior ; Game theory ; Altruism