Incentivizing Exploration with Heterogeneous Value of Money

Recently, Frazier et al. proposed a natural model for crowdsourced exploration of different a priori unknown options: a principal is interested in the long-term welfare of a population of agents who arrive one by one in a multi-armed bandit setting. However, each agent is myopic, so in order to incentivize him to explore options with better long-term prospects, the principal must offer the agent money. Frazier et al. showed that a simple class of policies called time-expanded are optimal in the worst case, and characterized their budget-reward tradeoff. The previous work assumed that all agents are equally and uniformly susceptible to financial incentives. In reality, agents may have different utility for money. We therefore extend the model of Frazier et al. to allow agents that have heterogeneous and non-linear utilities for money. The principal is informed of the agent's tradeoff via a signal that could be more or less informative. Our main result is to show that a convex program can be used to derive a signal-dependent time-expanded policy which achieves the best possible Lagrangian reward in the worst case. The worst-case guarantee is matched by so-called "Diamonds in the Rough" instances; the proof that the guarantees match is based on showing that two different convex programs have the same optimal solution for these specific instances. These results also extend to the budgeted case as in Frazier et al. We also show that the optimal policy is monotone with respect to information, i.e., the approximation ratio of the optimal policy improves as the signals become more informative.Comment: WINE 201

Information Design for Congested Social Services: Optimal Need-Based Persuasion

We study the effectiveness of information design in reducing congestion in social services catering to users with varied levels of need. In the absence of price discrimination and centralized admission, the provider relies on sharing information about wait times to improve welfare. We consider a stylized model with heterogeneous users who differ in their private outside options: low-need users have an acceptable outside option to the social service, whereas high-need users have no viable outside option. Upon arrival, a user decides to wait for the service by joining an unobservable first-come-first-serve queue, or leave and seek her outside option. To reduce congestion and improve social outcomes, the service provider seeks to persuade more low-need users to avail their outside option, and thus better serve high-need users. We characterize the Pareto-optimal signaling mechanisms and compare their welfare outcomes against several benchmarks. We show that if either type is the overwhelming majority of the population, information design does not provide improvement over sharing full information or no information. On the other hand, when the population is a mixture of the two types, information design not only Pareto dominates full-information and no-information mechanisms, in some regimes it also achieves the same welfare as the "first-best", i.e., the Pareto-optimal centralized admission policy with knowledge of users' types.Comment: Accepted for publication in the 21st ACM Conference on Economics and Computation (EC'20). 40 pages, 6 figure

Effectiveness of the transmission mechanism of monetary policy in the Euro area: the role of conventional and unconventional measures

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Challenges to generating political prioritization for adolescent sexual and reproductive health in Kenya: A qualitative study.

BackgroundDespite the high burden of adverse adolescent sexual and reproductive health (SRH) outcomes, it has remained a low political priority in Kenya. We examined factors that have shaped the lack of current political prioritization of adolescent SRH service provision.MethodsWe used the Shiffman and Smith policy framework consisting of four categories-actor power, ideas, political contexts, and issue characteristics-to analyse factors that have shaped political prioritization of adolescent SRH. We undertook semi-structured interviews with 14 members of adolescent SRH networks between February and April 2019 at the national level and conducted thematic analysis of the interviews.FindingsSeveral factors hinder the attainment of political priority for adolescent SRH in Kenya. On actor power, the adolescent SRH community was diverse and united in adoption of international norms and policies, but lacked policy entrepreneurs to provide strong leadership, and policy windows were often missed. Regarding ideas, community members lacked consensus on a cohesive public positioning of the problem. On issue characteristics, the perception of adolescents as lacking political power made politicians reluctant to act on the existing data on the severity of adolescent SRH. There was also a lack of consensus on the nature of interventions to be implemented. Pertaining to political contexts, sectoral funding by donors and government treasury brought about tension within the different government ministries resulting in siloed approaches, lack of coordination and overall inefficiency. However, the SRH community has several strengths that augur well for future political support. These include the diverse multi-sectoral background of its members, commitment to improving adolescent SRH, and the potential to link with other health priorities such as maternal health and HIV/AIDS.ConclusionIn order to increase political attention to adolescent SRH in Kenya, there is an urgent need for policy actors to: 1) create a more cohesive community of advocates across sectors, 2) develop a clearer public positioning of adolescent SRH, 3) agree on a set of precise approaches that will resonate with the political system, and 4) identify and nurture policy entrepreneurs to facilitate the coupling of adolescent SRH with potential solutions when windows of opportunity arise

MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response

This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL) approach to an incentive-based Demand Response (DR) program, which aims to maintain the capacity limits of the electricity grid and prevent grid congestion by financially incentivizing residential consumers to reduce their energy consumption. The proposed approach addresses the key challenge of coordinating heterogeneous preferences and requirements from multiple participants while preserving their privacy and minimizing financial costs for the aggregator. The participant agents use a novel Disjunctively Constrained Knapsack Problem optimization to curtail or shift the requested household appliances based on the selected demand reduction. Through case studies with electricity data from $25$ households, the proposed approach effectively reduced energy consumption's Peak-to-Average ratio (PAR) by $14.48$% compared to the original PAR while fully preserving participant privacy. This approach has the potential to significantly improve the efficiency and reliability of the electricity grid, making it an important contribution to the management of renewable energy resources and the growing electricity demand.Comment: 8 pages, IEEE Belgrade PowerTech, 202

Money That Costs Too Much: Regulating Financial Incentives

Money may not corrupt. But should we worry if it corrodes? Legal scholars in a range of fields have expressed concern about “motivational crowding-out,” a process by which offering financial rewards for good behavior may undermine laudable social motivations, like professionalism or civic duty. Disquiet about the motivational impacts of incentives has now extended to health law, employment law, tax, torts, contracts, criminal law, property, and beyond. In some cases, the fear of crowding-out has inspired concrete opposition to innovative policies that marshal incentives to change individual behavior. But to date, our fears about crowding-out have been unfocused and amorphous; our field lacks the language we need to speak precisely about these behavioral phenomena, and we have not examined when and why motivational crowding-out should prompt us to discontinue or temper incentive based schemes. Without a clear and nuanced picture of the processes, harms, and benefits of crowding-out, we may well be missing the mark. This Article canvasses the range of legal areas where crowding-out concerns arise, and it newly illuminates the specific harms that may be attributable to crowding-out effects. These hazards include reduced autonomy in the presence of incentives, a distinct set of behavioral inefficiencies, and the potential degradation of individual or social values. But this Article also challenges the view of crowding out as uniformly harmful, offering an alternative vision of potential crowding-out benefits, such as crowding out invidious motivations, increasing the predictability of agent activity, and bolstering the efficiency of future incentives. These benefits suggest that the precautionary principle, which would counsel against using incentives where crowding-out is possible – is inappropriate for this field. The Article also proposes a novel taxonomy to help guide regulatory responses to incentives that cause crowding-out. Different categories of incentives present different justifications for regulatory intervention and redesign, including autonomy concerns, efficiency concerns, and negative externalities imposed on third parties. By organizing incentives based on the relationship between the principal and agent, we can identify opportunities for regulators and incentive architects to redesign or limit incentive programs, to leave incentives in place, or to consider discontinuing incentive-based policies when money indeed “costs too much” in motivation