The Experts below are selected from a list of 45486 Experts worldwide ranked by ideXlab platform
Kyle C Meng - One of the best experts on this subject based on the ideXlab platform.
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using a free permit rule to forecast the marginal abatement cost of proposed climate policy
The American Economic Review, 2017Co-Authors: Kyle C MengAbstract:This paper develops a method for forecasting the marginal abatement cost (MAC) of climate policy using three features of the failed Waxman-Markey bill. First, the MAC is revealed by the price of traded permits. Second, the permit price is estimated using a regression discontinuity design (RDD) comparing stock returns of firms on either side of the policy’s free permit cutoff rule. Third, because Waxman-Markey was never implemented, I extend the RDD approach to incorporate Prediction Market prices which normalize estimates by policy realization probabilities. A final bounding analysis recovers a MAC range of $5 to $19 per ton CO2e.
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using a free permit rule to forecast the marginal abatement cost of proposed climate policy
The American Economic Review, 2017Co-Authors: Kyle C MengAbstract:Abstract This paper develops a method for forecasting the marginal abatement cost (MAC) of climate policy using three features of the failed Waxman-Markey bill. First, the MAC is revealed by the price of traded permits. Second, the permit price is estimated using a regression discontinuity design (RDD) comparing stock returns of firms on either side of the policy's free permit cutoff rule. Third, because Waxman-Markey was never implemented, I extend the RDD approach to incorporate Prediction Market prices which normalize estimates by policy realization probabilities. A final bounding analysis recovers a MAC range of $5 to $19 per ton CO2e. (JEL G12, G14, Q52, Q54, Q58)
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using a free permit rule to forecast the marginal abatement cost of proposed climate policy
Social Science Research Network, 2016Co-Authors: Kyle C MengAbstract:This paper develops a method for forecasting the marginal abatement cost (MAC) of climate policy using three features of the failed Waxman-Markey bill. First, the MAC is revealed by the price of traded permits. Second, the permit price is estimated using a regression discontinuity design (RDD) comparing stock returns of firms on either side of the policy’s free permit cutoff rule. Third, because Waxman-Markey was never implemented, I extend the RDD approach to incorporate Prediction Market prices which normalize estimates by policy realization probabilities. A final bounding analysis recovers a MAC range of $5 to $19 per ton CO2e.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Rahul Sami - One of the best experts on this subject based on the ideXlab platform.
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information aggregation in exponential family Markets
Economics and Computation, 2014Co-Authors: Jacob Abernethy, Sindhu Kutty, Sebastien Lahaie, Rahul SamiAbstract:We consider the design of Prediction Market mechanisms known as automated Market makers. We show that we can design these mechanisms via the mold of exponential family distributions, a popular and well-studied probability distribution template used in statistics. We give a full development of this relationship and explore a range of benefits. We draw connections between the information aggregation of Market prices and the belief aggregation of learning agents that rely on exponential family distributions. We develop a natural analysis of the Market behavior as well as the price equilibrium under the assumption that the traders exhibit risk aversion according to exponential utility. We also consider similar aspects under alternative models, such as budget-constrained traders.
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information aggregation in exponential family Markets
arXiv: Artificial Intelligence, 2014Co-Authors: Jacob Abernethy, Sindhu Kutty, Sebastien Lahaie, Rahul SamiAbstract:We consider the design of Prediction Market mechanisms known as automated Market makers. We show that we can design these mechanisms via the mold of \emph{exponential family distributions}, a popular and well-studied probability distribution template used in statistics. We give a full development of this relationship and explore a range of benefits. We draw connections between the information aggregation of Market prices and the belief aggregation of learning agents that rely on exponential family distributions. We develop a very natural analysis of the Market behavior as well as the price equilibrium under the assumption that the traders exhibit risk aversion according to exponential utility. We also consider similar aspects under alternative models, such as when traders are budget constrained.
Andrew B. Whinston - One of the best experts on this subject based on the ideXlab platform.
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the impact of social network structures on Prediction Market accuracy in the presence of insider information
The Missouri Review, 2014Co-Authors: Andrew B. WhinstonAbstract:This paper examines the effects of social network structures on Prediction Market accuracy in the presence of insider information through a randomized laboratory experiment. In the experiment, insider information is operationalized as signals on the state of nature with high precision. Motivated by the literature on insider information in the context of financial Markets, we test and confirm two characterizations of insider information in the context of Prediction Markets: abnormal performance and less diffusion. Experimental results suggest that a more balanced social network structure is crucial to the success of Prediction Markets, whereas network structures akin to star networks are ill suited to Prediction Markets. As compared with other network structures, insider information has less positive effects on Prediction Market accuracy in star networks. We also find that the bias of the public information has a larger negative effect on Prediction Market accuracy in star networks.
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A Twitter-Based Prediction Market: Social Network Approach
SSRN Electronic Journal, 2011Co-Authors: Liangfei Qiu, Huaxia Rui, Andrew B. WhinstonAbstract:Information aggregation mechanisms are designed explicitly for collecting and aggregating dispersed information. An excellent example of the use of this "wisdom of crowds" is a Prediction Market. The purpose of our Twitter-based Prediction Market is to suggest that carefully designed Market mechanisms can elicit and gather dispersed information that can improve our Predictions. We develop an information system that combines the power of Prediction Markets with the popularity of Twitter. Simulation results show that our network-embedded Prediction Market can produce better Predictions as a result of the information exchange in social networks and can outperform other non-networked Prediction Markets. We also demonstrate that forecasting errors decrease with the cost of acquiring information in a network-embedded Prediction Market.
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supply chain information sharing in a macro Prediction Market
Decision Support Systems, 2006Co-Authors: Zhiling Guo, Fang Fang, Andrew B. WhinstonAbstract:This paper aims to address supply chain partners' incentives for information sharing from an information systems design perspective. Specifically, we consider a supply chain characterized by N geographically distributed retailers who order a homogeneous product from one manufacturer. Each retailer's demand risk consists of two parts: a systematic risk part that affects all retailers and an idiosyncratic risk part that only has a local effect. We propose a macro Prediction Market to effectively elicit and aggregate useful information about systematic demand risk. We show that such information can be used to achieve accurate demand forecast sharing and better channel coordination in the supply chain system. Our Market-based framework extends the range of information sharing beyond the supply chain system. It also opens the door for other corporate risk management opportunities to hedge against aggregate economic risk.
Jacob Abernethy - One of the best experts on this subject based on the ideXlab platform.
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information aggregation in exponential family Markets
Economics and Computation, 2014Co-Authors: Jacob Abernethy, Sindhu Kutty, Sebastien Lahaie, Rahul SamiAbstract:We consider the design of Prediction Market mechanisms known as automated Market makers. We show that we can design these mechanisms via the mold of exponential family distributions, a popular and well-studied probability distribution template used in statistics. We give a full development of this relationship and explore a range of benefits. We draw connections between the information aggregation of Market prices and the belief aggregation of learning agents that rely on exponential family distributions. We develop a natural analysis of the Market behavior as well as the price equilibrium under the assumption that the traders exhibit risk aversion according to exponential utility. We also consider similar aspects under alternative models, such as budget-constrained traders.
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information aggregation in exponential family Markets
arXiv: Artificial Intelligence, 2014Co-Authors: Jacob Abernethy, Sindhu Kutty, Sebastien Lahaie, Rahul SamiAbstract:We consider the design of Prediction Market mechanisms known as automated Market makers. We show that we can design these mechanisms via the mold of \emph{exponential family distributions}, a popular and well-studied probability distribution template used in statistics. We give a full development of this relationship and explore a range of benefits. We draw connections between the information aggregation of Market prices and the belief aggregation of learning agents that rely on exponential family distributions. We develop a very natural analysis of the Market behavior as well as the price equilibrium under the assumption that the traders exhibit risk aversion according to exponential utility. We also consider similar aspects under alternative models, such as when traders are budget constrained.
Daniel E Oleary - One of the best experts on this subject based on the ideXlab platform.
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user participation in a corporate Prediction Market
Decision Support Systems, 2015Co-Authors: Daniel E OlearyAbstract:Corporate Prediction Markets allow companies to use external Market concepts to facilitate and support corporate decision making. Recently, Google, Microsoft, GE, Best Buy, and other firms have generated and used Prediction Markets as a means of gathering the "collective" intelligence of their employees. Since these Markets capture and aggregate information from employees and ultimately provide information for decision making, some researchers have referred to them as decision support systems or group decision support systems.Unfortunately, there has been limited theory development and empirical investigation of participation in corporate Prediction Markets. Accordingly, the purpose of this paper is to use theory generated about external investment Markets to investigate participation behavior in an internal corporate Market.Analysis of the number of unique traders by date and Market leads to a number of findings, including that Market traders apparently trade on specific information, there is a day-of-the-week effect of their participation, and participation is decreasing over time. Understanding the existence of such effects is important because they can influence the ability of the Market to provide sufficient, timely, and quality decision support information. Develops theory-based hypothesis generated in open financial Markets.Investigates patterns of corporate Prediction Market use over time.Isolates particular characteristics of use of a corporate Prediction Market.Compares the behavior of two different groups in a corporate Prediction Market.Finds day of the week, and other Market effects.