Surface-tension induced instabilities: Effects of lateral boundaries

Convection in circular and rectangular cylinders is analyzed. The governing equations and boundary conditions are formulated, linear and nonlinear stability theory are considered, and the physical implications of the theory are discussed

What Do We Expect from Our Friends?

We conduct a field experiment in a large real-world social network to examine how subjects expect to be treated by their friends and by strangers who make allocation decisions in modified dictator games. While recipients’ beliefs accurately account for the extent to which friends will choose more generous allocations than strangers (i.e. directed altruism), recipients are not able to anticipate individual differences in the baseline altruism of allocators (measured by giving to an unnamed recipient, which is predictive of generosity towards named recipients). Recipients who are direct friends with the allocator, or even recipients with many common friends, are no more accurate in recognizing intrinsically altruistic allocators. Recipient beliefs are significantly less accurate than the predictions of an econometrician who knows the allocator’s demographic characteristics and social distance, suggesting recipients do not have information on unobservable characteristics of the allocator.dictator games, beliefs, baseline altruism, directed altruism, social networks

Directed Altruism and Enforced Reciprocity in Social Networks: How Much is A Friend Worth?

We conduct field experiments in a large real-world social network to examine why decision makers treat friends more generously than strangers. Subjects are asked to divide surplus between themselves and named partners at various social distances, where only one of the decisions is implemented. In order to separate altruistic and future interaction motives, we implement an anonymous treatment where neither player is told at the end of the experiment which decision was selected for payment and a non-anonymous treatment where both players are told. Moreover, we include both games where transfers increase and decrease social surplus to distinguish between different future interaction channels including signaling one's generosity and enforced reciprocity, where the decision maker treats the partner to a favor because she can expect it to be repaid in the future. We can decompose altruistic preferences into baseline altruism towards any partner and directed altruism towards friends. Decision makers vary widely in their baseline altruism, but pass at least 50 percent more surplus to friends compared to strangers when decision making is anonymous. Under non-anonymity, transfers to friends increase by an extra 24 percent relative to strangers, but only in games where transfers increase social surplus. This effect increases with density of the network structure between both players, but does not depend on the average amount of time spent together each week. Our findings are well explained by enforced reciprocity, but not by signaling or preference-based reciprocity. We also find that partners' expectations are well calibrated to directed altruism, but that they ignore decision makers' baseline altruism. Partners with high baseline altruism have friends with higher baseline altruism and are therefore treated better.

What Do We Expect from Our Friends?

We conduct a field experiment in a large real-world social network to examine how subjects expect to be treated by their friends and by strangers who make allocation decisions in modified dictator games. While recipients beliefs accurately account for the extent to which friends will choose more generous allocations than strangers (i.e. directed altruism), recipients are not able to anticipate individual differences in the baseline altruism of allocators (measured by giving to an unnamed recipient, which is predictive of generosity towards named recipients). Recipients who are direct friends with the allocator, or even recipients with many common friends, are no more accurate in recognizing intrinsically altruistic allocators. Recipient be- liefs are significantly less accurate than the predictions of an econometrician who knows the allocators demographic characteristics and social distance, suggesting recipients do not have information on unobservable characteristics of the allocator.dictator games, beliefs, baseline altruism, directed altruism, social networks

Directed Altruism and Enforced Reciprocity in Social Networks

We conduct online field experiments in large real-world social networks in order to decompose prosocial giving into three components: (1) baseline altruism toward randomly selected strangers, (2) directed altruism that favors friends over random strangers, and (3) giving motivated by the prospect of future interaction. Directed altruism increases giving to friends by 52 percent relative to random strangers, while future interaction effects increase giving by an additional 24 percent when giving is socially efficient. This finding suggests that future interaction affects giving through a repeated game mechanism where agents can be rewarded for granting efficiency enhancing favors. We also find that subjects with higher baseline altruism have friends with higher baseline altruism.modified dictator games, directed altruism, enforced reciprocity, social networks

Managing Self-Confidence: Theory and Experimental Evidence

Evidence from social psychology suggests that agents process information about their own ability in a biased manner. This evidence has motivated exciting research in behavioral economics, but has also garnered critics who point out that it is potentially consistent with standard Bayesian updating. We implement a direct experimental test. We study a large sample of 656 undergraduate students, tracking the evolution of their beliefs about their own relative performance on an IQ test as they receive noisy feedback from a known data-generating process. Our design lets us repeatedly measure the complete relevant belief distribution incentive-compatibly. We find that subjects (1) place approximately full weight on their priors, but (2) are asymmetric, over-weighting positive feedback relative to negative, and (3) conservative, updating too little in response to both positive and negative signals. These biases are substantially less pronounced in a placebo experiment where ego is not at stake. We also find that (4) a substantial portion of subjects are averse to receiving information about their ability, and that (5) less confident subjects are causally more likely to be averse. We unify these phenomena by showing that they all arise naturally in a simple model of optimally biased Bayesian information processing.

Stationary solutions of driven fourth- and sixth-order Cahn-Hilliard type equations

New types of stationary solutions of a one-dimensional driven sixth-order Cahn-Hilliard type equation that arises as a model for epitaxially growing nano-structures such as quantum dots, are derived by an extension of the method of matched asymptotic expansions that retains exponentially small terms. This method yields analytical expressions for far-field behavior as well as the widths of the humps of these spatially non-monotone solutions in the limit of small driving force strength which is the deposition rate in case of epitaxial growth. These solutions extend the family of the monotone kink and antikink solutions. The hump spacing is related to solutions of the Lambert $W$ function. Using phase space analysis for the corresponding fifth-order dynamical system, we use a numerical technique that enables the efficient and accurate tracking of the solution branches, where the asymptotic solutions are used as initial input. Additionally, our approach is first demonstrated for the related but simpler driven fourth-order Cahn-Hilliard equation, also known as the convective Cahn-Hilliard equation

What Do We Expect from Our Friends?

Published in Journal of the European Economic Association, 2010, https://doi.org/10.1111/j.1542-4774.2010.tb00497.x</p

Reputation Agent: Prompting Fair Reviews in Gig Markets

Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets. Unfair reviews, created when requesters consider factors outside of a worker's control, are known to plague gig workers and can result in lost job opportunities and even termination from the marketplace. Our tool leverages machine learning to implement an intelligent interface that: (1) uses deep learning to automatically detect when an individual has included unfair factors into her review (factors outside the worker's control per the policies of the market); and (2) prompts the individual to reconsider her review if she has incorporated unfair factors. To study the effectiveness of Reputation Agent, we conducted a controlled experiment over different gig markets. Our experiment illustrates that across markets, Reputation Agent, in contrast with traditional approaches, motivates requesters to review gig workers' performance more fairly. We discuss how tools that bring more transparency to employers about the policies of a gig market can help build empathy thus resulting in reasoned discussions around potential injustices towards workers generated by these interfaces. Our vision is that with tools that promote truth and transparency we can bring fairer treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202