This dissertation consists of three essays that apply both economic theory and econometric methods to understand design and dynamics of institutions. In particular, it studies how institutions aggregate information and deal with uncertainty and attempts to derive implications for optimal institution design. Here is a brief summary of the essays. In many economic, political and social situations where the environment changes in a random fashion necessitating costly action we face a choice of both the timing of the action as well as choosing the optimal action. In particular, if the stochastic environment possesses the property that the next environmental change becomes either more or less likely as more time passes since the last change (in other words the hazard rate of environmental change is not constant over time), then the timing of the action takes on special importance. In the first essay, joint with Maxwell B Stinchcombe, we model and solve a dynamic decision problem in a semi-Markov environment. We find that if the arrival times for state changes do not follow a memoryless process, time since the last observed change of state, in addition to the current state, becomes a crucial variable in the decision. We characterize the optimal policy and the optimal timing of executing that policy in the differentiable case by a set of first order conditions of a relatively simple form. They show that both in the case of increasing and decreasing hazard rates, the optimal response may be to wait before executing a policy change. The intuitive explanation of the result has to do with the fact that waiting reveals information about the likelihood of the next change occurring, hence waiting is valuable when actions are costly. This result helps shed new light on the structure of optimal decisions in many interesting problems of institution design, including the fact that constitutions often have built-in delay mechanisms to slow the pace of legislative change. Our model results could be used to characterize optimal timing rules for constitutional amendments. The paper also contributes to generalize the methodology of semi-Markov decision theory by formulating a dynamic programming set-up that looks to solve the timing-of-action problem whereas the existing literature looks to optimize over a much more limited set of policies where the action can only be taken at the instant when the state changes. In the second essay, we extend our research to situations, where the current choice of action influences the future path of the stochastic process, and apply it to the legal framework surrounding environmental issues, particularly to the ‘Precautionary Principle' as applied to climate change legislation. We represent scientific uncertainty about environmental degradation using the concept of 'ambiguity' and show that ambiguity aversion generates a 'precautionary effect'. As a result, justification is provided for the Precautionary Principle that is different from the ones provided by expected utility theory. This essay serves both as an application of the general theoretical results derived in the first essay and also stands alone as an analysis of a substantive question about environmental law. Prediction markets have attracted public attention in recent years for making accurate predictions about election outcomes, product sales, film box office and myriad other variables of interest and many believe that they will soon become a very important decision support system in a wide variety of areas including governance, law and industry. For successful design of these markets, a thorough understanding of the theoretical and empirical foundations of such markets is necessary. But the information aggregation process in these markets is not fully understood yet. There remains a number of open questions. The third essay, joint with Robert Lieli, attempts to analyze the direction and timing of information flow between prices, polls, and media coverage of events traded on prediction markets. Specifically, we examine the race between Barack Obama and Hillary Clinton in the 2008 Democratic primaries for presidential nomination. Substantively, we ask the following question: (i) Do prediction market prices have information that is not reflected in viii contemporaneous polls and media stories? (ii) Conversely, do prices react to information that appears to be news for pollsters or is prominently featured by the media? Quantitatively, we construct time series variables that reflect the "pollster's surprise" in each primary election, measured as the difference between actual vote share and vote share predicted by the latest poll before the primary, as well as indices that describe the extent of media coverage received by the candidates. We carry out Granger Causality tests between the day-to-day percentage change in the price of the "Obama wins nomination" security and these information variables. Some key results from our exercise can be summarized as follows. There seems to be mutual (two-way) Granger causality between prediction market prices and the surprise element in the primaries. There is also evidence of one-way Granger causality in the short run from price changes towards media news indices. These results suggest that prediction market prices anticipate at least some of the discrepancy between the actual outcome and the latest round of polls before the election. Nevertheless, prices also seem to be driven partly by election results, suggesting that there is an element of the pollster’s surprise that is genuine news for the market as well.
