Presidential betting markets predict election outcomes more
accurately than polls because of their ability to effectively aggregate information. Empirical research and theory indicates that the result extends to other contexts. Betting markets, more formally called information markets, provide accurate predictions about future product sales, box office receipts, and other future events. Moreover, market predictions generally outperform other prediction mechanisms. This paper argues that empirical research and theory indicates that we should use information markets’ predictive power to make administrative decisions. In addition, it presents a model information market designed to help policy makers evaluate policies prior to their implementation by providing policy makers information about the policies’ effects in the form of market predictions. To design such a market, it is necessary to determine how the market should pay off bettors when the agency does not implement a policy because the market predicts it will have an adverse effect. The problem is that bets pay off based on the outcome of an event, but when the policy makers decide not to implement a policy, the policy has no effect and thus it is unclear how to compensate bettors. This paper shows that through clever market design it is possible to return the market price of a bet, prior to an agency’s decision not to implement the policy on which the bet depends, without fear of market manipulation. Consequently, even in cases where using market predictions to make administrative decisions appears problematic, it is possible.
Volume 92
Intellectual Property, Innovation, and Decision Architectures
This essay proposes a new way to assess the desirability of intellectual property rights.
Traditionally, intellectual property assignment is assessed based on a incentive/monopoly pricing tradeoff. I suggest they should be further assessed by their effects on the decision architectures surrounding the property right – their effects on how firms make product innovation decisions. The reason is that different decisional structures for product development can be are fundamental to the performance of firms, industries, and even the economy as a whole.
The organizational economics literature can help with this assessment. It makes an important and useful distinction between hierarchical (centralized) and polyarchical (decentralized) decision architectures. The key point of this paper is that government’s decisions with respect to property assignments can steer decision architectures toward a polyarchical or hierarchical architecture, respectively.
Each may be optimal in difference scenarios. In industries where technologies are stable and where the industry is flat or in decline, avoiding mistakes is more important, and uncertainty may be more limited, meaning that a hierarchy supported by strong rights may produce a more profitable outcome. Conversely, strong IP rights may undesirable in fast growing-industries where the technologies in flux, because overly centralized decision-making may block the emergence of the most innovative ideas.
Predictive Decisionmaking
In this Article, Professor Abramowicz identifies a regulatory strategy that he calls “predictive decisionmaking” and provides a framework for assessing it. In a predictive decisionmaking regime, public or private decisionmakers make explicit predictions, often of future legal decisions, rather than engage in normative analysis. Several scholars, particularly in recent years, have offered proposals that fit within the predictive decisionmaking paradigm, but have not noted the connection among these proposals. The Article highlights four different mechanisms on which predictive decisionmaking regimes may rely, including predictive standards, accuracy incentives, partial insurance requirements, and information markets. After identifying several advantages that predictive decisionmaking strategies may have over nonpredictive alternatives, the Article identifies several potential problems with predictive decisionmaking, and develops a simple analytical framework for assessing predictive decisionmaking proposals.