Forecasting with social media: evidence from Tweets on soccer matches

Social media is now used as a forecasting tool by a variety of firms and agencies. But how useful are such data in forecasting outcomes? Can social media add any in- formation to that produced by a prediction/betting market? We source 13.8m posts from Twitter, and combine them with contemporaneous Betfair betting prices, to fore- cast the outcomes of English Premier League soccer matches as they unfold. Using a micro-blogging dictionary to analyse the content of Tweets, we find that the aggregate tone of Tweets contains significant information not in betting prices, particularly in the immediate aftermath of goals and red cards

A Twitter-Based Prediction Market: Social Network Approach

Information aggregation mechanisms are designed explicitly for collecting and aggregating dispersed information. Prediction markets represent one of the best examples of how this kind of wisdom of the crowds can be used. We use a Twitter-based prediction market to suggest that carefully designed market mechanisms can bring to light trends in dispersed information that improves the accuracy of our predictions. The information system we are developing combines the power of prediction markets with the popularity of Twitter. Simulation results show that our network-embedded prediction market can produce better predictions using information exchange in social networks and can outperform other prediction markets that do not use social networks. We also demonstrate that as cost decreases and more and more agents acquire information, the prediction market prices fully incorporate all available information, and the forecasting performance of the network-embedded prediction market is better

Intuitive Biases in Choice Versus Estimation: Implications for the Wisdom of Crowds

Although researchers have documented many instances of crowd wisdom, it is important to know whether some kinds of judgments may lead the crowd astray, whether crowds’ judgments improve with feedback over time, and whether crowds’ judgments can be improved by changing the way judgments are elicited. We investigated these questions in a sports gambling context (predictions against point spreads) believed to elicit crowd wisdom. In a season-long experiment, fans wagered over $20,000 on NFL football predictions. Contrary to the wisdom-of-crowds hypothesis, faulty intuitions led the crowd to predict “favorites” more than “underdogs” against point spreads that disadvantaged favorites, even when bettors knew that the spreads disadvantaged favorites. Moreover, the bias increased over time, a result consistent with attributions for success and failure that rewarded intuitive choosing. However, when the crowd predicted game outcomes by estimating point differentials rather than by predicting against point spreads, its predictions were unbiased and wiser

Prediction Markets:A literature review 2014

In recent years, Prediction Markets gained growing interest as a forecasting tool among researchers as well as practitioners, which resulted in an increasing number of publications. In order to track the latest development of research, comprising the extent and focus of research, this article provides a comprehensive review and classification of the literature related to the topic of Prediction Markets. Overall, 304 relevant articles, published in the timeframe from 2007 through 2013, were identified and assigned to a herein presented classification scheme, differentiating between descriptive works, articles of theoretical nature, application-oriented studies and articles dealing with the topic of law and policy. The analysis of the research results reveals that more than half of the literature pool deals with the application and actual function tests of Prediction Markets. The results are further compared to two previous works published by Zhao, Wagner and Chen (2008) and Tziralis and Tatsiopoulos (2007a). The article concludes with an extended bibliography section and may therefore serve as a guidance and basis for further research. (250 WORDS

PREDICTIVE MODEL MARKETS: DESIGN PRINCIPLES FOR MANAGING ENTERPRISE-LEVEL ADVANCED ANALYTICS

As advanced analytics penetrate a wide range of business applications, companies face the challenge of managing analytics-based assets, such as predictive models. Tasks ahead include model selection, scoring and deployment planning. One way to optimize model selection is to tap the combined knowledge of company staff through a “prediction market,” a virtual market designed to reveal participants’ aggregate wisdom by seeing where people “invest” their money. In the context of predictive-model selection, this paper refers to such devices as predictive-model markets. This paper examines design possibilities for building experimental markets that can ultimately be used to test whether predictive-model markets will improve model selection and deployment. The researchers test two types of incentives for participation: economic and social. Study results indicate that such markets can effectively work using either; a surprising finding is that social incentives did not improve effectiveness when added to economic incentives

Population Curation in Swarms: Predicting Top Performers

In recent years, new Artificial Intelligence technologies have mimicked examples of collective intelligence occurring in the natural world including flocks of birds, schools of fish, and swarms of bees. One company in particular, Unanimous AI, built a platform (UNU Swarm) that enables a group of humans to make decisions as a single mind by forming a real-time closed-loop feedback system for individuals. This platform has proven the ability to amplify the predictive ability of groups of humans in realms including sports, medicine, politics, finance, and entertainment. Previous research has demonstrated it is possible to further enhance knowledge accumulation within a crowd through curation and bias methods applied to individuals in the crowd.\newline This study explores the efficacy of applying a machine learning pipeline to identify the top performing individuals in the crowd based on a structural profile of survey responses. The ultimate goal is to select these users as Swarm participants to improve the accuracy of the overall system. Unanimous AI provided 24 weeks of survey data collection consisting of 1,139 users from the NHL 2017-2018 season. By applying a machine learning pipeline, this study able to curate a crowd consisting of users that had an average z-score 0.309 and Wisdom of the Crowd prediction accuracy of 61.5%, which is 4.1% higher than a randomly selected crowd and 1.4% lower than Vegas favorite picks

Prediction Markets: A Systematic Review and Meta-Analysis

Prediction markets (PM) have drawn considerable attention in recent years as a tool for forecasting events. Studies surveying and examining relevant the trends of PM using traditional approaches have been reported in the literature. However, research using meta-analysis to review Prediction markets systems is very limited in Management Information System (MIS). This paper aimed to fill this gap by using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to study Prediction markets trends over the past decades. Our results are as follows. First, we find that shows that more than 64% of academic studies on Prediction markets are published in top journals such as Journal of the Association for Information Systems, Journal of Consumer Research and Information Systems Research. Second, we showed that Prediction markets applications can be can be divided into two groups: internal use PMS and general public usage. Finally, our significant meta-analysis result show that on average prediction markets is 79% more accurate than alternative forecast methods based

A new paradigm of knowledge management: Crowdsourcing as emergent research and development

Drawing from knowledge management theory, this paper argues that the knowledge aggregation problem poses a fundamental constraint to knowledge creation and innovation, and offers a potential solution to this problem. Specific consequences of innovation failure include the failure of research and development to deliver new medicines to address threats such as widespread and increasing antibiotic resistance, the rise of airborne multidrug-resistant or totally drug-resistant tuberculosis, as well as a lack of new drugs to deal with emerging threats such as Ebola. Persistent constraints to knowledge creation exist in the form of market failure, or the failure of profit-seeking models of innovation to internalise the positive externalities associated with innovations, as well as academic failure, or the failure of academic research to provide much needed innovations to address societal problems. However, a lack of theory exists as to how to transcend these constraints to knowledge aggregation. This paper presents a probabilistic theoretical framework of innovation, suggesting that the ‘wisdom of the crowd’, or emergent properties of problem-solving, may emerge as a function of scale when crowdsourcing principles are applied to research and development. It is argued in this paper that the consequences of a lack of knowledge of innovation failure are already upon us, and that a radical new approach to knowledge management and innovation is needed.Keywords: probabilistic innovation, knowledge management, innovation, crowdsourcing, crowdsourced R&

Essays in Applied Economics

During my studies and research I have always been fascinated by the variety of problems economists can study using empirical and theoretical methods. Understanding how incentives, the (non)-availability of information, or strategic interaction shape outcomes in many areas of life is the key goal of my academic work. Naturally, many of these ideas emerged from studying the functioning of markets or situations in which market failures lead to inefficiencies. However, applying economic insights to various aspects of society has opened up a rich set of research questions. The very fact that many of these aspects have an impact on important areas of society has motivated me to research in different areas, applying economic thinking and methodology. This view is reflected in the choice of research questions for this thesis. It comprises a study of how the support of crowds, in theory mostly irrelevant to a fully rational agent, affects the performance of professional football players. I argue that choking under pressure is a relevant dimension in such environments. I consequently study the role of betting markets in pricing novel insights on the performance of such athletes in the absence of crowds due to the coronavirus pandemic. I also study a crucial field of modern societies that is, due to the presence of externalities, largely organized outside of markets: academic research. I contribute to the quantitative understanding of the role of information acquisition across different scientific fields. Lastly, motivated by the ubiquitous problem of controlling the spread of the novel Coronavirus SARS-CoV-2, I build on an epidemic model and discuss some of the trade-offs policy-makers face when deciding on quarantine policies. With this thesis I want to contribute to a better understanding of how markets and organizations do and should approach the complexity of incomplete information and human behavior. In the first chapter, I show that crowd presence may be a two-edged sword not benefiting all teams equally -- some instead seem display what is known as choking under pressure. In chapter 2, I document that this effect is not adequately incorporated into market prices by bookmakers, contradicting market efficiency. In the third chapter, I discuss the important policy problem of allocating scarce resources to different research projects of ex-ante unobservable quality. Commonly, expert knowledge is used to identify projects considered fund-worthy. I document empirically that experts across scientific fields differ with respect to their ability in reliably evaluating the merit of their peers' work (noise). In particular, scholars in natural sciences display higher agreement than their colleagues from other fields. Lastly, the fourth chapter introduces the role of incomplete information in a workhorse epidemiological model. In the case of covid-19, symptoms together with tests as means of retrieving information on health status, can reduce the economic and social burden of the pandemic. I argue that targeted quarantine measures are superior to general lockdowns if the the policy-maker is endowed with rich information

A General Approach for Predicting the Behavior of the Supreme Court of the United States

Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time evolving random forest classifier which leverages some unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.Comment: version 2.02; 18 pages, 5 figures. This paper is related to but distinct from arXiv:1407.6333, and the results herein supersede arXiv:1407.6333. Source code available at https://github.com/mjbommar/scotus-predict-v