Low-Distortion Social Welfare Functions

Work on implicit utilitarian voting advocates the design of preference aggregation methods that maximize utilitarian social welfare with respect to latent utility functions, based only on observed rankings of the alternatives. This approach has been successfully deployed in order to help people choose a single alternative or a subset of alternatives, but it has previously been unclear how to apply the same approach to the design of social welfare functions, where the desired output is a ranking. We propose to address this problem by assuming that voters’ utilities for rankings are induced by unknown weights and unknown utility functions, which, moreover, have a combinatorial (subadditive) structure. Despite the extreme lack of information about voters’ preferences, we show that it is possible to choose rankings such that the worst-case gap between their social welfare and that of the optimal ranking, called distortion, is no larger (up to polylogarithmic factors) than the distortion associated with much simpler problems. Through experiments, we identify practical methods that achieve nearoptimal social welfare on average.

Identification With Additively Separable Heterogeneity

This paper provides nonparametric identification results for a class of latent utility models with additively separable unobservable heterogeneity. These results apply to existing models of discrete choice, bundles, decisions under uncertainty, and matching. Under an independence assumption, such models admit a representative agent. As a result, we can identify how regressors alter the desirability of goods using only average demands. Moreover, average indirect utility (“welfare”) is identified without needing to specify or identify the distribution of unobservable heterogeneity.

Subset Selection Via Implicit Utilitarian Voting

How should one aggregate ordinal preferences expressed by voters into a measurably superior social choice? A well-established approach -- which we refer to as implicit utilitarian voting -- assumes that voters have latent utility functions that induce the reported rankings, and seeks voting rules that approximately maximize utilitarian social welfare. We extend this approach to the design of rules that select a subset of alternatives. We derive analytical bounds on the performance of optimal (deterministic as well as randomized) rules in terms of two measures, distortion and regret. Empirical results show that regret-based rules are more compelling than distortion-based rules, leading us to focus on developing a scalable implementation for the optimal (deterministic) regret-based rule. Our methods underlie the design and implementation of RoboVote.org, a not-for-profit website that helps users make group decisions via AI-driven voting methods.

The utilization of social media for youth outreach engagement: A case study

This article reports a case study that aimed to explore and conceptualize the use of social media in youth outreach engagement in Hong Kong. It used “social media affordance” as a heuristic lens to inform the analysis. This concept refers to the latent utility of social media, which is dependent on both the intrinsic features of social media and the perceptions of the users. In this study, this includes the reviewability, visibility, authoring capabilities, and associative capabilities of social media. We conducted a thematic analysis on data collected including: official service statistics, meeting notes, focus group discussions, and the practitioners’ presentation notes. Major and minor themes were organized with reference to the outreach techniques noted by the practitioners. Significant affordance(s) supporting each technique were identified. The findings revealed specific outreach techniques used by the practitioners including: (i) online searches, (ii) initial encounters before direct verbal dialogue, (iii) ice-breaking via multi-modal communication, and (iv) snowballing. Compared to youth outreach in physical settings, online outreach demonstrates greater visibility of interactions and improved immediacy, but also implies losing privacy, and changes in professional identity. Moreover, the findings showed that each technique was arguably related more to particular affordances than some others, denoting a tacit knowledge informing practitioners to make their choices. These observations inspired further discussion about the significance of a reliable conceptual framework that can guide social workers to identify, compare, and further adapt technologies to enhance their current practice.

Retailer, Manufacturers, and Individual Consumers: Modeling the Supply Side in the Ketchup Marketplace

The authors construct a model of the local ketchup market in a Texas city that accounts for household, manufacturer, and retailer decisions. That is, the model develops both demand and supply sides of the market. The authors model the demand side through a latent utility framework that allows for a no-purchase option. Accounting for both sides of the market enables the authors to check for any endogeneity problems on the demand side. They model the supply side through the profit-maximizing decisions of the manufacturers and a multiproduct retailer. Accounting for both the retailer and the manufacturer decisions enables the authors to evaluate the degree of manufacturer competition, retailer–manufacturer interactions, and retailer product-category pricing. Given the model assumptions and the market being studied, the authors find that not accounting for demand endogeneity can create bias in the estimation, the retailer seems to price below the static profit-maximizing prices for two of the three brands, and the inferred marginal wholesale prices are below the equilibrium uniform wholesale prices for two brands. The authors discuss the results with regard to channel bargaining, quantity discounts, and retailer category pricing.