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PolicyOracle: Nowcasting Expectations on Policy Indices

In our globally interconnected social and economic systems, policy decision processes could be greatly benefited by instruments able to aggregate and interpret heterogeneous and dispersed information among individuals and proactively sense possible problems concerning policy related matters. For instance, knowledge of the inflation expectations of the population may help the European Central Bank to devise a more effective monetary policy or access to expectations on CO2 emissions may inform governments to adjust energy related policies. Several institutions collect expectations and periodically (monthly, quarterly etc.) release aggregated indices. The most widely used methods are typically based on surveys among experts (e.g. the ECB survey of professional forecasters (Garcia, 2003)), households or consumers and generally follow closely the economic situation and even anticipate turning points in the economic cycle (Manski, 2004). While the usefulness of monitoring expectations is not questionable, existing approaches commonly rely on survey-based methods and suffer from two major limitations. First they report indices with a lag: the survey data for a given period is generally released about halfway through the next period, while reported data are typically revised several months later. Second, they provide a rather partial view of a more complex dynamics. The reason is that such methodologies are not incentive-compatible (Hurwicz, 1972) and, therefore, might be of low precision in revealing the ‘real’ preferences and expectations of economic agents.Pursuing the necessity of informing the policy decision process, we set out to use Information Markets (IMs) as an institution which efficiently aggregates diverse information using the price mechanism. IMs are considered an example of collective intelligence because of their capability to aggregate and nowcast information that arrives with a lag by making use of specifically designed contracts that yield payments based on the outcome of uncertain future events (Wolfers & Zitzewitz, 2004). Contract prices provide a reasonable estimate of what the traders in aggregate believe to be the probability of the aforementioned events. IMs are characterized by their accuracy, easy deployment, and ability to dynamically incorporate new information available to traders by continuously adjusting an event’s price (Snowberg et al., 2012). However despite the expected benefits, the public sector seems rather reluctant to introduce IMs for improving public decision making. Hanson (2006) proposed information markets as a new tool that will revolutionize governance, while Ledyard (2006) and Hahn and Tetlock (2005) describe a framework and identify the main characteristics an information market should fulfill in order to perform well in situations relevant for policy decision making. Possibly one of the reasons for the public sector’s reluctance to experiment with information markets (at least in the US) is related to the controversy and political debate raised around the Policy Analysis Market (Hanson 2007). Recently, however, the use of information markets has been suggested as a tool which can foster the participation of citizens in European public policy (Millard et al., 2009) or support macro-economic forecasts for governments (Teschner et al., 2011). In this paper, we describe our approach for an IM that supports aggregation of information from experts and interested stakeholders as well as citizens at large by considering their opinions and discussions as expressed in social media. Our approach is instantiated in a work-in-progress system called PolicyOracle.