Our aim of this paper was to create a method for comparing the overall relative efficiency of a prediction market for the English football league Premier League and a prediction market for the Swedish football league Allsvenskan. The purpose of this was to see how the overall turnover of a prediction market affects the efficiency of it. We conclude that while the implied probability of the two markets on average corresponds well to the win frequency, the Premier League prediction market has statistically significant lower variation than Allsvenskan. The method we created can also be used to test the relative prediction accuracy of any two prediction markets/bookmakers given enough observations.

Prediction markets have recently gained favour with the academic and business communities. Prediction markets have evolved a long way from their basic beginnings as friendly wagers among friends to become large scale markets connecting traders from around the world. They have been adopted into many large and dynamic corporations that require up to the minute information that can keep up with their business. Organisations like Google, HP, Yahoo! and Best Buy have been experimenting with prediction markets for demand forecasting tasks. Governments have also been using markets, although not always as successfully. The U.S. government looked at PAM which became the terrorist futures market in the post 9/11 world. This did not appeal to the American populous and it has since been withdrawn. Through technological advancements the capabilities and availability of prediction markets has grown. With this the interest in how they work and what can be done to improve the accuracy of the markets. This research looked at the inclusion of a deliberative technique to the markets to improve that accuracy of the market. For this research, markets that made use of discussion boards were used. They were compared against traditional markets, which had no means of communication between traders.The research took the form of a quantitative comparison between the two market types. Data was acquired from the Iowa Electronics Market (IEM) and Inkling Public Markets. The findings from this research indicate that there was a significant difference with α=0.012 for the markets at close. This indicated that there was a significant between the traditional (control) and non-traditional (experimental groups) markets from descriptive statistics it was indicated that the traditional markets performed better in the prediction tasks. The conclusions of this research indicate that allowing traders to communicate and see the actions of others creates group biases which impacts on their independence when making trades and thus on the performance of the market.Copyright / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted

Information technology and social media have been a driving force in the economy and have transformed all aspects of business in recent decades. Understanding social networks is necessary to evaluate their impacts and examine key business issues involving information and technological innovations. The dissertation contains three chapters exploring those issues. In the first chapter, I propose an optimal procurement mechanism for mobile data offloading, covering both technological and business aspects. The unprecedented growth of cellular traffic driven by web surfing, video streaming, and cloud-based services is creating challenges for cellular service providers to fulfill the unmet demand. My present work contributes to the existing literature by developing an analytical model, which considers the unique challenge of integrating the longer range cellular resource and shorter range WiFi hotspots. In the second chapter, I examine the effect of a social network on prediction markets using a controlled laboratory experiment. In prediction markets, people place bets on events that they think are most likely to happen, thus revealing in a sense the nature of their private information. Through a randomized experiment, I show that when the cost of information acquisition is low, a social-network-embedded prediction market outperforms a non-networked prediction market. The third chapter studies different forms of social learning in the context of location-based networks: observational learning and the saliency effect. In recent years, the location-sensing mobile devices offer geographic location capabilities to share users' information about their locations with their friends. In our context, observational learning corresponds to the fact that "check-ins" made by friends help users learn the quality information of a venue; the saliency effect refers to that check-ins lead some of the uninformed consumers to discover a new venue. / text

Since the rise of Bitcoin in 2008, many have speculated about the scope of blockchain technology applications. Prediction markets, i.e. markets in which uncertain outcomes of future events are tradeable, is such an application; blockchain technology may offer several technological attributes that may facilitate prediction market implementations. This study describes and compares the platforms of three organizations that build blockchain prediction market platforms: Augur, Gnosis and Stox. By this, we provide a pertinent overview of current blockchain prediction market applications. Additionally, we conduct interviews with three Swedish blockchain experts clarifying blockchain technology strengths and weaknesses in relation to prediction markets. We identify five factors that are essential for prediction markets to aggregate and reflect information accurately: many actors participating, no actors being prevented from participating, a trustworthy setting function, freedom to create new contracts, and transparency. We conclude that blockchain technology has attributes that facilitate future prediction market implementations in accordance with these requirements. However, blockchain scalability issues pose a key challenge. / Sedan Bitcoins introduktion 2008 har många spekulerat kring omfattningen av blockkedjeteknologins tillämpningsområden. Prediktionsmarknader (eng. prediction markets), d.v.s. marknader i vilka det går att spekulera i osäkra resultat av framtida händelser, är ett sådant tillämpningsområde; blockkedjeteknologi kan tillhandahålla aspekter som främjar implementationer av prediktionsmarknader. Denna artikel beskriver och jämför plattformarna som tillhandahålls av tre organisationer som använder sig av blockkedjeteknologi for att bygga prediktions­marknadsplattformar: Augur, Gnosis och Stox. Genom detta tillhandahåller vi en helhetssyn över nuvarande prediktionsmarknadsplattformar som bygger på blockkedjeteknologi. Dessutom genomför vi intervjuer med tre svenska blockkedjeteknologiexperter, detta för att klargöra blockkedjeteknologis styrkor och svagheter i förhållande till prediktionsmarknader. Vi identifierar fem faktorer som är essentiella för prediktionsmarknaders förmåga att framgångsrikt aggregera och reflektera information: att många aktorer deltar, att inga aktorer är förhindrade från att delta, en tillförlitlig funktion för avgörande av utfall, frihet att skapa nya kontrakt, samt transparens. Vi drar slutsatsen att blockkedjeteknologi, med avseende på dessa faktorer, har egenskaper som förenklar implementationen av prediktionsmarknader. Å andra sidan utgör blockkedjors skalbarhetsproblem en signifikant utmaning.

In this thesis I offer new results on how we can acquire, reward, and use accurate predictions of future events. Some of these results are entirely theoretical, improving our understanding of strictly proper scoring rules (Chapter 3), and expanding strict properness to include cost functions (Chapter 4). Others are more practical, like developing a practical cost function for the [0, 1] interval (Chapter 5), exploring how to design simple and informative prediction markets (Chapter 6), and using predictions to make decisions (Chapter 7). / Engineering and Applied Sciences

Economic agents often face situations, where there are multiple competing fore- casts available. Despite five decades of research on forecast combinations, most of the methods introduced so far fail to outperform the equal weights forecast combination in empirical applications. In this study, we gather a wide spectrum of forecast combination methods and reexamine these findings in two different classical economic times series forecasting applications. These include out-of- sample combining forecasts from the ECB Survey of Professional Forecasters and forecasts of the realized volatility of the U.S. Treasury futures log-returns. We asses the performance of artificial predictions markets, a class of machine learning methods, which has not yet been applied to the problem of combin- ing economic times series forecasts. Furthermore, we propose a new simple method called Market for Kernels, which is designed specifically for combining time series forecasts. We found that equal weights can be significantly out- performed by several forecast combinations, including Bates-Granger methods and artificial prediction markets in the ECB Survey of Professional Forecasters application and by almost all examined forecast combinations in the financial application. We also found that the Market for Kernels forecast...

New product development and management are critical to the long-term success of the firm. New product development is also an area where the firm needs to improve performance. Two important new product decisions are selecting new concepts and estimating their future market potential and demand. Forecasting is a critical activity in supporting these two decisions. Unfortunately, forecasting is an activity where firms often struggle to be proficient. Recent advances in forecasting methods offer opportunities for improvement. One of the techniques is prediction markets, an emerging methodology that taps collective intelligence. Despite widely reported application and promise of prediction markets, they have yet to be adopted in marketing practice or examined in marketing academia. This dissertation addresses two research questions: do prediction markets produce better marketing forecasts than methods traditionally employed by firms, and if they do, how do they do it? To answer these research questions, two field studies are completed. The first is an empirical test of prediction markets compared to traditional forecasting methods implemented within a Fortune 100 firm. The second, based on a post survey, is an analysis of how market knowledge factors in combination with prediction markets design factors produce superior results. Study I finds that prediction markets do provide superior results in 67% of the forecasts and reduce error levels and ranges. Study II finds that out of several design factors, prediction market forecast accuracy is driven most by new information acquisition and knowledge heterogeneity. These findings contribute to MSI 2012-2014 Research Priorities and calls in the marketing literature to develop, better, real-time, intelligent decision support tools to help solve problems of the big data era and support improved demand forecasting. / Business Administration/Marketing

千年以來，人類不斷遭遇各種疫病的侵襲，流行速度更勝戰火蔓延，影響整體人類重大，然而隨著醫學知識的進步與衛生環境的改善，許多傳染病已經受到控制乃至根絕，但是生活周遭仍然面臨諸多威脅生命健康的潛藏危機，如果稍有疏失或不慎，傳染病不僅對於人體造成傷害，甚至恐將危害社會、經濟和政治層面，而近年的SARS、H1N1等流行病毒皆造成全球恐慌。 防疫工作重點在於及早掌握疫情趨勢以利制定相關因應政策，目前各國對於傳染病的掌握主要透過層層監測系統與歷史平均，藉以判斷該年特定傳染病流行與散佈程度。這些方法受到各種人為與環境因素影響，導致推估疫情成效有限之外，同時所得資料無法直接反應未來疫情，因此導致各國相關單位逐漸嘗試其他預測方法。 近年應用預測市場機制預測疫情模式引起公衛領域的重視，相關學術期刊與著名雜誌相繼介紹此一新興模式，同時肯定其在預測傳染病方面的成效與貢獻，而美國和台灣政府部門先後透過此項機制改善現有防疫體系的不足。那麼，預測市場用以預測疫情的成效是否確實如同其在眾多領域取得的成效一樣出眾？鑑於前述問題，本研究分別透過質化與量化的方式發掘公衛、醫學或流病學等其他領域對於「傳染病預測市場」是否能夠成為有效的預測機制或是成為常規的參考方法，結果證實使用的疾管局人員與參與的專業醫事人員認為「傳染病預測市場」確實可以應用於我國疫情預測的層面，但是兩者意見具有程度的差異。 / For centuries, the spread of various diseases damage countless human beings, which surpass wars in the world. Those diseases not only endanger people’s life, but also invade the other dimensions, including society, economic and politics. With the advancement of medical knowledge and the improvement of public health, many infectious diseases have been brought under control and even eradicated. But humans still face and experience threats from pandemic viruses such as SARS and H1N1 constantly. Epidemic prevention work focuses on understanding the variation of situation as soon as possible. Then governments can set up suitable decisions and policies based on epidemic situation. Though the monitoring system and the historical average are the mainstream to control the trends of infections for related departments, scientists believe that the two methods are subject to humans and environmental factors. In other words, it is difficult to draw effective information and direct response of the future trends from present methods. And it leads to national units gradually try other epidemic forecasting methods. In recent years, using prediction markets to predict flu causes the attention of public health. Thus academic journals and well-known magazines not only introduce this application but approve its effectiveness and contribution in predicting infectious diseases. The departments of US and Taiwan have tried to improve the deficiencies of the existing prevention system through prediction markets. Is this application really as successful as PM in many issues and fields? To response the question, this research intends to through qualitative and quantitative ways respectively to explore the evaluations on Epidemic Prediction Markets behind public health, medical, epidemiology, etc. The result confirms that CDC staff and health workers identify the feasibility of Epidemic Prediction Markets, but with the degree of variation.

Ma thèse de doctorat traite de la sélection de projets d'innovation en entreprises, en utilisant les marchés de prédiction comme mécanisme de sélection alternatif. Le processus d'innovation et son évaluation sont des activités ayant des répercussions sur la croissance et le développement. L’évidence montre que les méthodes habituelles d'évaluation et de sélection de projets d’innovation, comme le processus en entonnoir, ne sont pas rentables. Proposer une méthode plus efficace contribuera de manière significative à une meilleure allocation des ressources. Dans la première partie de ma thèse, je teste les prévisions du marché de prédiction contre celles des experts. Dans la deuxième, j'examine les aspects comportementaux de la prise de décision sur le marché de prédiction entrepreneurial, notamment comment le biais d’optimisme influence les décisions des traders. J’ai mené pour ces parties des expériences avec des sujets humains. Dans la troisième partie, j'examine les propriétés et éléments clés des marchés de prédiction et fourni une chronique et une classification d’articles sur les contributions les plus importantes de la littérature dans ce sujet. / My PhD thesis deals with selection of corporate and entrepreneurial innovation projects, using prediction markets as an alternative selection mechanism. Innovation process and its evaluation are two very important economic activities with repercussions for growth and development. Available evidence strongly suggests that conventional evaluation and selection methods, such as development funnel in corporate setting or decisions of Venture Capital firms in entrepreneurial one, do not yield cost-effective results. Coming up with an efficient and cost-effective method would contribute significantly to better resource allocation and social welfare. In the first part of the thesis, I test the prediction market predictions against experts’. In the second part, I examine behavioral aspects of decision-making in entrepreneurial prediction market setting, particularly how optimism bias influences traders’ decisions in prediction market. I conducted experiments with human subjects for the first two parts. In the third part of the thesis, I examine the most important elements and properties of prediction markets and provide a survey of most important contributions to prediction market literature, together with the classification and list of articles in major categories.

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. / text