Optimising Trade-offs Among Stakeholders in Ad Auctions

We examine trade-offs among stakeholders in ad auctions. Our metrics are the revenue for the utility of the auctioneer, the number of clicks for the utility of the users and the welfare for the utility of the advertisers. We show how to optimize linear combinations of the stakeholder utilities, showing that these can be tackled through a GSP auction with a per-click reserve price. We then examine constrained optimization of stakeholder utilities. We use simulations and analysis of real-world sponsored search auction data to demonstrate the feasible trade-offs, examining the effect of changing the allowed number of ads on the utilities of the stakeholders. We investigate both short term effects, when the players do not have the time to modify their behavior, and long term equilibrium conditions. Finally, we examine a combinatorially richer constrained optimization problem, where there are several possible allowed configurations (templates) of ad formats. This model captures richer ad formats, which allow using the available screen real estate in various ways. We show that two natural generalizations of the GSP auction rules to this domain are poorly behaved, resulting in not having a symmetric Nash equilibrium or having one with poor welfare. We also provide positive results for restricted cases.Comment: 18 pages, 10 figures, ACM Conference on Economics and Computation 201

The Impact of Situational Constraints, Role Stressors, and Commitment on Employee Altruism

This study investigated relations between 3 work-related stressors (role ambiguity, role conflict, and organizational constraints) and altruistic behavior in the workplace. It was predicted that each stressor would be negatively related to altruism and that these relations would be moderated by affective commitment (AC). Data from 144 incumbent-supervisor dyads revealed that all 3 stressors; were weakly and negatively related to altruism. Two of these relationships were moderated by AC, although not as predicted. Organizational constraints were positively related to altruism among those reporting high levels of AC but negatively related among those reporting low levels of AC. The pattern was exactly opposite for role conflict. Implications of these findings are discussed

Approximately Minwise Independence with Twisted Tabulation

A random hash function $h$ is $\varepsilon$-minwise if for any set $S$, $|S|=n$, and element $x\in S$, $\Pr[h(x)=\min h(S)]=(1\pm\varepsilon)/n$. Minwise hash functions with low bias $\varepsilon$ have widespread applications within similarity estimation. Hashing from a universe $[u]$, the twisted tabulation hashing of P\v{a}tra\c{s}cu and Thorup [SODA'13] makes $c=O(1)$ lookups in tables of size $u^{1/c}$. Twisted tabulation was invented to get good concentration for hashing based sampling. Here we show that twisted tabulation yields $\tilde O(1/u^{1/c})$-minwise hashing. In the classic independence paradigm of Wegman and Carter [FOCS'79] $\tilde O(1/u^{1/c})$-minwise hashing requires $\Omega(\log u)$-independence [Indyk SODA'99]. P\v{a}tra\c{s}cu and Thorup [STOC'11] had shown that simple tabulation, using same space and lookups yields $\tilde O(1/n^{1/c})$-minwise independence, which is good for large sets, but useless for small sets. Our analysis uses some of the same methods, but is much cleaner bypassing a complicated induction argument.Comment: To appear in Proceedings of SWAT 201

Blended delivery of imagery rescripting for childhood ptsd:A case study during the covid 19 pandemic

[Background] Despite the growing evidence that trauma-focused treatments can be applied as first-line approaches for individuals with childhood trauma-related PTSD (Ch-PTSD), many therapists are still reluctant to provide trauma-focused treatments as a first-choice intervention for individuals with Ch-PTSD, especially by telehealth. The current manuscript will therefore give an overview of the evidence for the effectiveness of trauma-focused therapies for individuals with Ch-PTSD, the delivery of trauma-focused treatments via telehealth, and a case example on how a specific form of trauma focused therapy: Imagery Rescripting (ImRs) can be applied by telehealth. [Method] This article presents a clinical illustration of a blended telehealth trajectory of imagery rescripting (ImRs) Ch-PTSD delivered during the COVID-19 pandemic. [Results] The presented case shows that ImRs can be safely and effectively performed by telehealth for ch-PTSD, no stabilization phase was needed and only seven sessions were needed to drastically reduce Ch-PTSD and depressive symptoms, and to increase quality of life. [Conclusion] This case report shows the effectiveness of ImRs by telehealth for Ch-PTSD, which gives hope and additional possibilities to reach out to patients with ch-PTDS. Telehealth treatment might have some of advantages for specific patients, especially, but certainly not only, during the pandemic

Efficient Equilibria in Polymatrix Coordination Games

We consider polymatrix coordination games with individual preferences where every player corresponds to a node in a graph who plays with each neighbor a separate bimatrix game with non-negative symmetric payoffs. In this paper, we study $\alpha$-approximate $k$-equilibria of these games, i.e., outcomes where no group of at most $k$ players can deviate such that each member increases his payoff by at least a factor $\alpha$. We prove that for $\alpha \ge 2$ these games have the finite coalitional improvement property (and thus $\alpha$-approximate $k$-equilibria exist), while for $\alpha < 2$ this property does not hold. Further, we derive an almost tight bound of $2\alpha(n-1)/(k-1)$ on the price of anarchy, where $n$ is the number of players; in particular, it scales from unbounded for pure Nash equilibria ($k = 1)$ to $2\alpha$ for strong equilibria ($k = n$). We also settle the complexity of several problems related to the verification and existence of these equilibria. Finally, we investigate natural means to reduce the inefficiency of Nash equilibria. Most promisingly, we show that by fixing the strategies of $k$ players the price of anarchy can be reduced to $n/k$ (and this bound is tight)

Using Group Model Building to Understand Factors That Influence Childhood Obesity in an Urban Environment

Background: Despite increased attention, conventional views of obesity are based upon individual behaviors, and children and parents living with obesity are assumed to be the primary problem solvers. Instead of focusing exclusively on individual reduction behaviors for childhood obesity, greater focus should be placed on better understanding existing community systems and their effects on obesity. The Milwaukee Childhood Obesity Prevention Project is a community-based coalition established to develop policy and environmental change strategies to impact childhood obesity in Milwaukee, Wisconsin. The coalition conducted a Group Model Building exercise to better understand root causes of childhood obesity in its community. Methods: Group Model Building is a process by which a group systematically engages in model construction to better understand the systems that are in place. It helps participants make their mental models explicit through a careful and consistent process to test assumptions. This process has 3 main components: (1) assembling a team of participants; (2) conducting a behavior-over-time graphs exercise; and (3) drawing the causal loop diagram exercise. Results: The behavior-over-time graph portion produced 61 graphs in 10 categories. The causal loop diagram yielded 5 major themes and 7 subthemes. Conclusions: Factors that influence childhood obesity are varied, and it is important to recognize that no single solution exists. The perspectives from this exercise provided a means to create a process for dialogue and commitment by stakeholders and partnerships to build capacity for change within the community

Cooperative AI: machines must learn to find common ground

Artificial-intelligence assistants and recommendation algorithms interact with billions of people every day, influencing lives in myriad ways, yet they still have little understanding of humans. Self-driving vehicles controlled by artificial intelligence (AI) are gaining mastery of their interactions with the natural world, but they are still novices when it comes to coordinating with other cars and pedestrians or collaborating with their human operators