Judgment Aggregation in Creative Production: Evidence from the Movie Industry

We study a novel, low-cost approach to aggregating judgment from a large number of industry experts on ideas that they encounter in their normal course of business. Our context is the movie industry, in which customer appeal is difficult to predict and investment costs are high. The Black List, an annual publication, ranks unproduced scripts based on anonymous nominations from film executives. This approach entails an inherent trade-off: Low participation costs enable high response rates, but nominations lack standard criteria, and which voters see which ideas is unobservable and influenced by various factors. Despite these challenges, we find that such aggregation is predictive: Listed scripts are substantially more likely to be released than observably similar, but unlisted, scripts, and, conditional on release and investment levels, listed scripts generate higher box-office revenues. We also find that this method mitigates entry barriers for less-experienced writers, as (i) their scripts are more likely to be listed than those by experienced writers and to rank higher if listed and (ii) within scripts by less-experienced writers, being listed is associated with a higher release rate. Yet, the gap in release probabilities relative to experienced writers remains large, even for top-ranked scripts. These results can be explained by the premise that scripts from less-experienced writers are more visible among eligible voters than scripts from experienced writers. This highlights idea visibility as an important determinant of votes and surfaces the trade-offs, as well as potential limitations, associated with such methods. This paper was accepted by Ashish Arora, entrepreneurship and innovation.

Strategic manipulation in judgment aggregation under higher-level reasoning

We analyse the incentives of individuals to misrepresent their truthful judgments when engaged in collective decision-making. Our focus is on scenarios in which individuals reason about the incentives of others before choosing which judgments to report themselves. To this end, we introduce a formal model of strategic behaviour in logic-based judgment aggregation that accounts for such higher-level reasoning as well as the fact that individuals may only have partial information about the truthful judgments and preferences of their peers. We find that every aggregation rule must belong to exactly one of three possible categories: it is either (i) immune to strategic manipulation for every level of reasoning, or (ii) manipulable for every level of reasoning, or (iii) immune to manipulation only for every kth level of reasoning, for some natural number k greater than 1.

The Complexity Landscape of Outcome Determination in Judgment Aggregation

We provide a comprehensive analysis of the computational complexity of the outcome determination problem for the most important aggregation rules proposed in the literature on logic-based judgment aggregation. Judgment aggregation is a powerful and flexible framework for studying problems of collective decision making that has attracted interest in a range of disciplines, including Legal Theory, Philosophy, Economics, Political Science, and Artificial Intelligence. The problem of computing the outcome for a given list of individual judgments to be aggregated into a single collective judgment is the most fundamental algorithmic challenge arising in this context. Our analysis applies to several different variants of the basic framework of judgment aggregation that have been discussed in the literature, as well as to a new framework that encompasses all existing such frameworks in terms of expressive power and representational succinctness.

Designing Participatory Budgeting Mechanisms Grounded in Judgment Aggregation

We introduce a new approach for designing rules for participatory budgeting, the problem of deciding on the use of public funds based directly on the views expressed by the citizens concerned. The core idea is to embed instances of the participatory budgeting problem into judgment aggregation, a powerful general-purpose framework for modelling collective decision making. Taking advantage of the possibilities offered by judgment aggregation, we enrich the familiar setting of participatory budgeting with additional constraints, namely dependencies between projects and quotas regarding different types of projects. We analyse the rules obtained both in algorithmic and in axiomatic terms.