Incentivizing Exploration with Selective Data Disclosure

We study the design of rating systems that incentivize (more) efficient social learning among self-interested agents. Agents arrive sequentially and are presented with a set of possible actions, each of which yields a positive reward with an unknown probability. A disclosure policy sends messages about the rewards of previously-chosen actions to arriving agents. These messages can alter agents' incentives towards exploration, taking potentially sub-optimal actions for the sake of learning more about their rewards. Prior work achieves much progress with disclosure policies that merely recommend an action to each user, but relies heavily on standard, yet very strong rationality assumptions. We study a particular class of disclosure policies that use messages, called unbiased subhistories, consisting of the actions and rewards from a subsequence of past agents. Each subsequence is chosen ahead of time, according to a predetermined partial order on the rounds. We posit a flexible model of frequentist agent response, which we argue is plausible for this class of "order-based" disclosure policies. We measure the success of a policy by its regret, i.e., the difference, over all rounds, between the expected reward of the best action and the reward induced by the policy. A disclosure policy that reveals full history in each round risks inducing herding behavior among the agents, and typically has regret linear in the time horizon $T$. Our main result is an order-based disclosure policy that obtains regret $\tilde{O}(\sqrt{T})$. This regret is known to be optimal in the worst case over reward distributions, even absent incentives. We also exhibit simpler order-based policies with higher, but still sublinear, regret. These policies can be interpreted as dividing a sublinear number of agents into constant-sized focus groups, whose histories are then revealed to future agents

Incentivizing Exploration with Linear Contexts and Combinatorial Actions

We advance the study of incentivized bandit exploration, in which arm choices are viewed as recommendations and are required to be Bayesian incentive compatible. Recent work has shown under certain independence assumptions that after collecting enough initial samples, the popular Thompson sampling algorithm becomes incentive compatible. We give an analog of this result for linear bandits, where the independence of the prior is replaced by a natural convexity condition. This opens up the possibility of efficient and regret-optimal incentivized exploration in high-dimensional action spaces. In the semibandit model, we also improve the sample complexity for the pre-Thompson sampling phase of initial data collection.Comment: International Conference on Machine Learning (ICML) 202

Can the EU taxonomy for sustainable activities help upscale investments into urban nature-based solutions?

We analyze the potential of the European Union (EU) Taxonomy (ET) for Sustainable Activities to mobilize investments for the sustainability transition toward urban nature-based solutions (UNBS). We map the current investment landscape of UNBS in Europe and combine this mapping with document analysis of UNBS inclusion in the ET to understand how the ET might help overcome the well-documented barriers to UNBS finance. We suggest that the ET has a legitimizing effect on UNBS as climate investments, which can support their uptake, but also conclude that only some UNBS subtypes are explicitly included when they fit with existing investment classes. In particular, the ET (1) disregards innovative - and specifically urban - UNBS types and (2) fails to provide incentives for investments that can deliver multiple sustainable objectives, which would enhance the investment case for UNBS. Since the current investment landscape of UNBS is characterized by a strong presence of public actors and a high incidence of co-financing, we recommend that public actors leverage the ET to obtain private funding for UNBS via (green) bond issuance and public-private co-finance instruments. Our analysis indicates that the ability of the ET to upscale investments for specific sustainability transitions depends on the interplay among their current investment landscapes, specific financing barriers, and explicit inclusion in the ET

Balancing Producer Fairness and Efficiency via Prior-Weighted Rating System Design

Online marketplaces use rating systems to promote the discovery of high-quality products. However, these systems also lead to high variance in producers' economic outcomes: a new producer who sells high-quality items, may unluckily receive one low rating early on, negatively impacting their future popularity. We investigate the design of rating systems that balance the goals of identifying high-quality products (efficiency) and minimizing the variance in economic outcomes of producers of similar quality (individual producer fairness). We show that there is a trade-off between these two goals: rating systems that promote efficiency are necessarily less individually fair to producers. We introduce prior-weighted rating systems as an approach to managing this trade-off. Informally, the system we propose sets a system-wide prior for the quality of an incoming product; subsequently, the system updates that prior to a posterior for each producer's quality based on user-generated ratings over time. We show theoretically that in markets where products accrue reviews at an equal rate, the strength of the rating system's prior determines the operating point on the identified trade-off: the stronger the prior, the more the marketplace discounts early ratings data (increasing individual fairness), but the slower the platform is in learning about true item quality (so efficiency suffers). We further analyze this trade-off in a responsive market where customers make decisions based on historical ratings. Through calibrated simulations, we show that the choice of prior strength mediates the same efficiency-consistency trade-off in this setting. Overall, we demonstrate that by tuning the prior as a design choice in a prior-weighted rating system, platforms can be intentional about the balance between efficiency and producer fairness.Comment: 12 pages, 8 figures, submitted to TheWebConf 202

Paving the way to net-zero : identifying environmental sustainability factors for business model innovation through carbon disclosure project data

Net-zero emission targets are crucial, given the environmental impact of the food and beverage industries. Our study proposes an environmentally focused Sustainable Business Model (SBM) using data from 252 food, beverage, and tobacco companies that reported to the Carbon Disclosure Project (CDP). We investigated the risks, opportunities, business strategies, emission reduction initiatives, and supply chain interactions associated with climate change by analyzing their qualitative answers using the NVivo software. Following the grounded theory approach, we identified the Environmental Sustainability Factors (ESFs) that support businesses in meeting pollution reduction targets. The ESFs were integrated with Osterwalder’s business model canvas to create an archetype focused on delivering “net-zero” or “carbon neutral” value to customers. The model’s efficacy is enhanced by the advantages and motivations of environmental collaborations. The paper provides critical support for sustainability theories and assists Small and Medium Enterprises (SMEs) to develop strategic business models for net-zero emission targets

Resolving corporate bribery through deferred prosecution agreements:Lessons from the US, UK and France for China

While bribery is designated as a criminal offense in most jurisdictions, the enforcement of anti-bribery laws in the corporate context is far from satisfactory. The weak enforcement can be mainly attributed to the practical challenges of doing so. Benefiting from deferred prosecution agreements (DPAs), the U.S., UK and French authorities have significantly ramped up their anti-bribery enforcement and encouraged corporate self-policing activities. Inspired by the foreign DPA developments, China’s prosecutorial authorities have been actively promoting the compliance non-prosecution program (CNP) since 2020. Introduced amid the Covid-19 pandemic and the ever-intensive U.S.-China trade conflicts, the CNP aims to mitigate the adverse economic implications of corporate criminal enforcement and foster corporate compliance.Combining legal doctrinal research, comparative research and insights from the law and economics literature, this thesis provides an overview of the DPA regimes in the U.S., UK and France and the CNP in China. It analyzes the advantages and weakness of the DPA programs in the three jurisdictions, aiming to draw lessons for developing the Chinese version of DPA program to address corporate bribery. Meanwhile, it also identifies the reasons for the inactive role played by the corporations in China’s anti-bribery movement and the challenges caused for the authorities in the anti-bribery enforcement. It is proposed that a Chinese version of DPA program be established based on the existing CNP to resolve corporate bribery cases. When designing and applying the Chinese version of DPA program and complementary regimes, special attention should be paid to deterrence, rehabilitation, and individual accountability.<br/

Resolving corporate bribery through deferred prosecution agreements:Lessons from the US, UK and France for China

While bribery is designated as a criminal offense in most jurisdictions, the enforcement of anti-bribery laws in the corporate context is far from satisfactory. The weak enforcement can be mainly attributed to the practical challenges of doing so. Benefiting from deferred prosecution agreements (DPAs), the U.S., UK and French authorities have significantly ramped up their anti-bribery enforcement and encouraged corporate self-policing activities. Inspired by the foreign DPA developments, China’s prosecutorial authorities have been actively promoting the compliance non-prosecution program (CNP) since 2020. Introduced amid the Covid-19 pandemic and the ever-intensive U.S.-China trade conflicts, the CNP aims to mitigate the adverse economic implications of corporate criminal enforcement and foster corporate compliance.Combining legal doctrinal research, comparative research and insights from the law and economics literature, this thesis provides an overview of the DPA regimes in the U.S., UK and France and the CNP in China. It analyzes the advantages and weakness of the DPA programs in the three jurisdictions, aiming to draw lessons for developing the Chinese version of DPA program to address corporate bribery. Meanwhile, it also identifies the reasons for the inactive role played by the corporations in China’s anti-bribery movement and the challenges caused for the authorities in the anti-bribery enforcement. It is proposed that a Chinese version of DPA program be established based on the existing CNP to resolve corporate bribery cases. When designing and applying the Chinese version of DPA program and complementary regimes, special attention should be paid to deterrence, rehabilitation, and individual accountability.<br/

Against Notice Skepticism in Privacy (and Elsewhere)

What follows is an exploration of innovative new ways to deliver privacy notice. Unlike traditional notice that relies upon text or symbols to convey information, emerging strategies of “visceral” notice leverage a consumer’s very experience of a product or service to warn or inform. A regulation might require that a cell phone camera make a shutter sound so people know their photo is being taken. Or a law could incentivize websites to be more formal (as opposed to casual) wherever they collect personal information, as formality tends to place people on greater guard about what they disclose. The thesis of this Article is that, for a variety of reasons, experience as a form of privacy disclosure is worthy of further study before we give in to calls to abandon notice as a regulatory strategy in privacy and elsewhere. In Part I, the Article examines the promise of radical new forms of experiential or visceral notice based in contemporary design psychology. This Part also compares and contrasts visceral notice to other regulator strategies that seek to “nudge” or influence consumer or citizen behavior. Part II discusses why the further exploration of visceral notice and other notice innovation is warranted. Part III explores potential challenges to visceral notice—for instance, from the First Amendment—and lays out some thoughts on the best regulatory context for requiring or incentivizing visceral notice. In particular, this Part highlights the potential of safe harbors and goal-based rules, i.e., rules that look to the outcome of a notice strategy rather than dictate precisely how notice must be delivered. This Article uses online privacy as a case study for several reasons. First, notice is among the only affirmative obligations that companies face with respect to privacy—online privacy is a quintessential notice regime. Second, the Internet is a context in which notice is widely understood to have failed, but where the nature of digital services means that viable regulatory alternatives are few and poor. Finally, the fact that websites are entirely designed environments furnishes unique opportunities for the sorts of untraditional interventions explored in Part I