LEARNING-BY-DOING AND PROJECT CHOICE: A DYNAMIC STRUCTURAL MODEL OF CROWDSOURCING

This paper studies determinants of project choice in online crowdsourcing contests using a unique dataset from the world’s largest competitive software development portal. Particular attention is given to the strategic roles of learning and forward-looking behavior in influencing contestants’ decisions. We use a structural dynamic discrete programming (DDP) model to conduct our analysis and adopt a Bayesian approach to estimation. Our preliminary results provide evidence of learning-by-doing influencing propensities of users to choose projects of different types. The value of the parameter of intertemporal substitution that we identify suggests that while users are forward-looking, the aggregate behavior is far from fully rational. We attribute that result to mix of forward-looking and myopic users in the population

Modeling Volatility in Prediction Markets

Nowadays, there is a significant experimental evidence of excellent ex-post predictive accuracy in certain types of prediction markets, such as markets for elections. This evidence shows that prediction markets are efficient mechanisms for aggregating information and are more accurate in forecasting events than traditional forecasting methods, such as polls. Interpretation of prediction market prices as probabilities has been extensively studied in the literature, however little attention so far has been given to understanding volatility of prediction market prices. In this paper, we present a model of a prediction market with a binary payoff on a competitive event involving two parties. In our model, each party has some underlying ``ability'' process that describes its ability to win and evolves as an Ito diffusion. We show that if the prediction market for this event is efficient and accurate, the price of the corresponding contract will also follow a diffusion and its instantaneous volatility is a particular function of the current claim price and its time to expiration. We generalize our results to competitive events involving more than two parties and show that volatilities of prediction market contracts for such events are again functions of the current claim prices and the time to expiration, as well as of several additional parameters (ternary correlations of the underlying Brownian motions). In the experimental section, we validate our model on a set of InTrade prediction markets and show that it is consistent with observed volatilities of contract returns and outperforms the well-known GARCH model in predicting future contract volatility from historical price data. To demonstrate the practical value of our model, we apply it to pricing options on prediction market contracts, such as those recently introduced by InTrade. Other potential applications of this model include detection of significant market moves and improving forecast standard errors

Modeling Dependency in Prediction Markets

In the last decade, prediction markets became popular forecasting tools in areas ranging from election results to movie revenues and Oscar nominations. One of the features that make prediction markets particularly attractive for decision support applications is that they can be used to answer what-if questions and estimate probabilities of complex events. Traditional approach to answering such questions involves running a combinatorial prediction market, what is not always possible. In this paper, we present an alternative, statistical approach to pricing complex claims, which is based on analyzing co-movements of prediction market prices for basis events. Experimental evaluation of our technique on a collection of 51 InTrade contracts representing the Democratic Party Nominee winning Electoral College Votes of a particular state shows that the approach outperforms traditional forecasting methods such as price and return regressions and can be used to extract meaningful business intelligence from raw price data

Social Status and Badge Design

Many websites rely on user-generated content to provide value to consumers. These websites typically incentivize participation by awarding users badges based on their contributions. While these badges typically have no explicit value, they act as symbols of social status within a community. In this paper, we consider the design of badge mechanisms for the objective of maximizing the total contributions made to a website. Users exert costly effort to make contributions and, in return, are awarded with badges. A badge is only valued to the extent that it signals social status and thus badge valuations are determined endogenously by the number of users who earn each badge. The goal of this paper is to study the design of optimal and approximately badge mechanisms under these status valuations. We characterize badge mechanisms by whether they use a coarse partitioning scheme, i.e. awarding the same badge to many users, or use a fine partitioning scheme, i.e. awarding a unique badge to most users. We find that the optimal mechanism uses both fine partitioning and coarse partitioning. When status valuations exhibit a decreasing marginal value property, we prove that coarse partitioning is a necessary feature of any approximately optimal mechanism. Conversely, when status valuations exhibit an increasing marginal value property, we prove that fine partitioning is necessary for approximate optimality

Modeling Volatility in Prediction Markets

Nowadays, there is a significant experimental evidence of excellent ex-post predictive accuracy in certain types of prediction markets, such as markets for elections. This evidence shows that prediction markets are efficient mechanisms for aggregating information and are more accurate in forecasting events than traditional forecasting methods, such as polls. Interpretation of prediction market prices as probabilities has been extensively studied in the literature, however little attention so far has been given to understanding volatility of prediction market prices. In this paper, we present a model of a prediction market with a binary payoff on a competitive event involving two parties. In our model, each party has some underlying ``ability'' process that describes its ability to win and evolves as an Ito diffusion. We show that if the prediction market for this event is efficient and accurate, the price of the corresponding contract will also follow a diffusion and its instantaneous volatility is a particular function of the current claim price and its time to expiration. We generalize our results to competitive events involving more than two parties and show that volatilities of prediction market contracts for such events are again functions of the current claim prices and the time to expiration, as well as of several additional parameters (ternary correlations of the underlying Brownian motions). In the experimental section, we validate our model on a set of InTrade prediction markets and show that it is consistent with observed volatilities of contract returns and outperforms the well-known GARCH model in predicting future contract volatility from historical price data. To demonstrate the practical value of our model, we apply it to pricing options on prediction market contracts, such as those recently introduced by InTrade. Other potential applications of this model include detection of significant market moves and improving forecast standard errors

Deriving the Pricing Power of Product Features by Mining Consumer Reviews

The growing pervasiveness of the Internet has changed the way that consumers shop for goods. Increasingly, user-generated product reviews serve as a valuable source of information for customers making product choices online. While there is a significant body of theory on multi-attribute choice under uncertainty, the literature that examines product reviews has not built on this stream of theory for a variety of reasons. Typically, the impact of product reviews has been incorporated by numeric variables representing the valence and volume of reviews. In this paper we posit that the information embedded in product reviews cannot be captured by a single scalar value. Rather, we argue that product reviews are multifaceted and hence, the textual content of product reviews is an important determinant of consumers' choices, over and above the valence and volume of reviews. We provide a text mining technique that allows us to incorporate text in choice and panel data models by decomposing textual reviews into segments, evaluating different product features. We test our approach on a unique dataset collected from Amazon, and demonstrate how it can be used to learn consumers' relative preferences for different product features. The dataset used contains three different groups of products (digital cameras, camcorders, PDAs), associated sales data and consumer review data gathered over a 15-month period. Additionally, we present and discuss two experimental techniques that can be used to alleviate the problem of data sparsity and of omitted variables: the first technique models consumer opinions as elements of a tensor product of independent feature and evaluation spaces and the second technique clusters rare opinions based on pointwise mutual information. The paper concludes by discussing the managerial relevance of this work as a tool for extracting actionable business intelligence from user-generated content

Deriving the Pricing Power of Product Features by Mining Consumer Reviews

The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumer-generated product reviews have become a valuable source of information for customers, who read the reviews and decide whether to buy the product based on the information provided. In this paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics that models consumer product reviews as elements in a tensor product of feature and evaluation spaces. We then impute the quantitative impact of consumer reviews on product demand as a linear functional from this tensor product space. We demonstrate how to use a low-dimension approximation of this functional to significantly reduce the number of model parameters, while still providing good experimental results. We evaluate our technique using a data set from Amazon.com consisting of sales data and the related consumer reviews posted over a 15-month period for 242 products. Our experimental evaluation shows that we can extract actionable business intelligence from the data and better understand the customer preferences and actions. We also show that the textual portion of the reviews can improve product sales prediction compared to a baseline technique that simply relies on numeric data

Deriving the Pricing Power of Product Features by Mining Consumer Reviews

The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumer-generated product reviews have become a valuable source of information for customers, who read the reviews and decide whether to buy the product based on the information provided. In this paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics that models consumer product reviews as elements in a tensor product of feature and evaluation spaces. We then impute the quantitative impact of consumer reviews on product demand as a linear functional from this tensor product space. We demonstrate how to use a low-dimension approximation of this functional to significantly reduce the number of model parameters, while still providing good experimental results. We evaluate our technique using a data set from Amazon.com consisting of sales data and the related consumer reviews posted over a 15-month period for 242 products. Our experimental evaluation shows that we can extract actionable business intelligence from the data and better understand the customer preferences and actions. We also show that the textual portion of the reviews can improve product sales prediction compared to a baseline technique that simply relies on numeric data