=Paper=
{{Paper
|id=Vol-1148/paper10
|storemode=property
|title=A Research Agenda for Prediction Markets
|pdfUrl=https://ceur-ws.org/Vol-1148/paper10.pdf
|volume=Vol-1148
}}
==A Research Agenda for Prediction Markets==
https://ceur-ws.org/Vol-1148/paper10.pdf
A Research Agenda for Prediction Markets
Patrick Buckley1
1
Kemmy Business School, University of Limerick, Ireland
Patrick.Buckley@ul.ie
Abstract. This paper describes the use of prediction markets as tools for ena-
bling collective intelligence. Their benefits are explored and current applica-
tions are elucidated. Moving on from this, key open research questions from
the literature are identified, and a research agenda that can address these issues
is introduced.
Keywords: Prediction Markets, Collective Intelligence
1 Introduction
Prediction markets are a relatively novel form of decision making. The core insight
upon which they are based is that a market mechanism can be used to enable two
processes which are crucial to effective decision making. First, the provision of indi-
vidual rewards to participants prompts truthful information revelation. Second, asset
price movement within a market provides a mechanism that can be adapted to support
information aggregation. When deployed using Information Technology (IT), predic-
tion markets can trivially scale to hundreds or even thousands of participants. This
scalability enables collaborative decision making on a scale that many other group
decision making mechanisms would find prohibitive. They provide a method of gen-
erating collective intelligence that can draw upon the wisdom of large, disparate
crowds.
This paper is structured as follows. In section 2, we introduce the concept of pre-
diction markets. We particularly focus on the theorised benefits of prediction markets
from a decision making perspective and elucidate current applications of prediction
markets. As befits a relatively novel innovation, there are many open research
questions regarding prediction markets, which are discussed in section 3. In sec-
tion 4 we present a brief description of a methodology which can provide data to
investigate a research agenda in prediction market that can address some of the pre-
viously identified issues. We conclude in section 5 by calling for help in operational-
izing this research agenda.
2 Prediction Markets
2.1 Prediction Markets
Prediction markets are “markets that are designed and run for the primary purpose of
mining and aggregating information scattered among traders and subsequently using
this information in the form of market values in order to make predictions about spe-
cific future events.” [1, p. 75]. The theoretical roots of prediction markets can be
found in Hayek’s conceptualization of markets as near perfect transmitters of infor-
mation [2]. This perspective on market operation led to the formulation of the effi-
cient market hypothesis, which has been expressed as stating that stock “prices at any
time ‘fully reflect’ all available information” [3, p. 383]. There are a number of forms
of the efficient market hypothesis, including the weak, semi-strong and strong form,
which make more or less demanding claims as to the efficiency of information trans-
mission within markets [4], [5]. While it is relatively trivial to point to specific exam-
ples of market failure, in general, speculative markets such as those in stocks, com-
modities and future options do a credible, if imperfect job of aggregating relevant
information into market prices [6]. This position is backed by a substantial body of
empirical evidence [5], [7, 9].
A prediction market is created by offering a contract on the outcome of a future
event of interest for sale to a group of participants. For example, suppose an organisa-
tion wishes to forecast whether or not a project will reach its next milestone on time.
The organisation could create a contract PROJ, which will pay a holder €1 on the date
of the milestone if the milestone is reached or €0 otherwise. The organisation would
set the initial price of the contract at 50 cents and then offer it for sale to individuals
participating in the project. Under these circumstances, if an individual believes that
the project is likely to reach its milestone, they will buy the contract in the expectation
of receiving a greater reward in the future. Equally, if a rational individual believes
the project will not reach its milestone, then they will sell (or ‘short’) the con-
tract, taking the profit immediately. Individuals buying or selling the contracts be-
ing offered will have the effect of moving the price of the contract.
This two-outcome model can be easily extended to allow for the creation of con-
tracts across a range of disjoint outcomes. For example, a prediction market can be
created which asks participants to forecast what will be the most successful product
from a range of products. They can also be used to allow participants to forecast val-
ues rather than select from a particular set of options. As an example, participants may
be asked to forecast the total sales of a particular product.
Prediction markets differ from traditional financial markets in two important ways.
First, prediction markets operate by enabling participants to trade contracts, whose
value is dependent upon the outcome of a future uncertain event [10]. In a prediction
market, the trade of contracts in a market place allows participants to exchange infor-
mation. The trade of contracts also acts as a decision mechanism, since the price of
the contract at any point in time can be viewed as the consensus opinion of all the
participants in the market as to the likelihood of an event occurring. In this way, the
trade of contracts enables the underlying processes of communication and decision
making that is required to allow group decision making to occur [11].
The second distinguishing characteristic of a prediction market is that its primary
concern is the elicitation of information [12]. In the modern world, many markets
exist that allow participants to trade assets whose value is dependent upon an uncer-
tain future event. While these markets can be viewed as prediction markets from a
certain perspective, in general this paper will follow the guidelines proposed by Wolf-
ers and Zitewitz [13]. It steers away from markets where the primary role is enhancing
the enjoyment of an external event through taking on risk. Similarly, markets whose
primary rationale for existence is that they enable the hedging of financial risk will
not be considered prediction markets.
2.2 Benefits of Prediction Markets
Researchers have identified a number of theoretical benefits of prediction markets
over comparable information aggregation mechanisms such as polls or expert groups
[14]. First, prediction markets provide incentives for truthful information revelation.
Second, they provide an algorithm for automatically communicating and aggregating
information. Third, prediction markets implicitly weight the information supplied by
participants. Fourth, prediction markets can scale efficiently to very large groups, a
major advantage over other forms of group decision making, particularly where rele-
vant information is widely dispersed. Fifth, prediction markets can operate in real-
time over a long period of time. Finally, prediction markets can be designed in such a
way as to allow for trader anonymity.
Prediction markets are instantiated by offering contracts for trade whose value is
dependent upon the outcome of a future event. Contracts are specified in the format,
“Pay €X if event Y occurs”. Individual participants buy and sell these contracts. Re-
wards for correct forecasts accrue to the individual who holds the contract.
This individualization of reward creates an incentive for individuals to hold contracts
in events they believe are likely to occur [15]. By providing an individualized incen-
tive some of the challenges associated with information revelation in other domains
can be ameliorated [10]. In a deliberative group, individuals may have little incentive
to reveal private information, since any benefits will accrue to the group as whole. By
providing information to the group, they bestow benefits on others without any reward
to themselves, and possibly facing high private costs [16]. The provision of a direct
financial incentive to an individual can serve as a counter weight to the emotional,
political and professional factors that may inhibit truthful information revelation in a
group setting. Since participants are rewarded for accurate decisions, all other things
being equal, the provision of individualized incentives should promote information
search [17]–[19].
The second characteristic of prediction markets is that they implicitly contain an
algorithm for information aggregation. The operation of the market in contracts, and
the trading it facilitates automatically creates the equilibrium price which is used as a
proxy for estimates about the event of interest [10]. By allowing experts to trade with
each other, prediction markets allow disparate opinions and beliefs to be aggregated
into a coherent, consistent whole [18]. As well as providing a mechanism for aggre-
gating the private beliefs of individuals, prediction markets can also enable indi-
vidual participants to extract information from observing market estimates [20],
and correct biases in publicly available information [21].
Several authors point out that prediction markets implicitly weight the information
supplied by participants [18], [22], [23]. If participants are more confident of their
beliefs in a particular topic, they will be willing to buy more of the relevant contracts,
and vice versa. The ability of participants to choose the level of their investment al-
lows them to indicate their confidence in their information in a manner which is au-
tomatically accommodated by the aggregation algorithm.
The nature of the market structure also means that prediction markets can scale to
very large groups [15]. When considering a market that utilizes information technolo-
gy to enable trading, the only real limits on the number of participants are computa-
tional. This means that prediction markets potentially have lower running costs, par-
ticular if they are in operation over a period of time [24]. Most of the overheads in
deploying prediction markets are involved in setting up the market and attracting par-
ticipants. It also means that prediction markets can be created that can utilize partici-
pants from outside traditional organizational boundaries, recruiting participants from
suppliers, customers and other stakeholders in order to improve the decision making
process.
Prediction markets can operate in real-time [10], [24]. This gives them a significant
advantage over other comparable information aggregation methods such as polls.
Finally, prediction markets can be designed in such a way as to allow trader anonymi-
ty [25]. Power relationships and social interactions in group decision making are often
seen as responsible for some of the weaknesses of group decision making [11]. The
utility of this attribute can vary, but the ability to enable it demonstrates the flexibility
of prediction markets.
2.3 Applications of Prediction Markets
Markets which share the defining characteristics of prediction markets have existed
for hundreds of years. Specific examples from the literature include markets on Papal
elections in 16th century Italy, parliamentary elections in 18th and 19th century
Britain and American presidential elections [26], [27]. Modern interest in prediction
markets is generally held to have begun with the establishment of the Iowa Electronic
Market (IEM) in 1988, which is often seen as the first implementation of a prediction
market. (Joyce Berg et al. 2008a). Since then academic and practitioner interest in
prediction markets has continued to grow [1].
Modern operational prediction markets can be broadly divided into three categories.
The first subdivision is that between public and private prediction markets. A public
prediction market is one which invites participation from the general public. A
private prediction market is one created by a sponsor which seeks to recruit partic-
ipants from a specific, albeit potentially very large population. Within public predic-
tion markets, some prediction markets operate using real currency. Participants invest
their own money in the market, and gain or lose according to their performance.
Other public prediction markets use virtual currency to enable trading. Table 1 lists
some exemplars of these prediction markets.
Type Example
Public (real currency) Intrade, http://www.intrade.com
Betfair, http://www.betfair.com
Iowa Electronic Market, http://tippie.uiowa.edu/
Public (virtual currency) The Hollywood Stock Exchange,
Hub-dub, http://www.hubdub.com
Newsfutures, http://www.lumenogic.com
Foresight Exchange, http://www.ideosphere.com
Private Qmarkets, http://www.qmarkets.com
Inkling markets, http://inkling.com
Crowdcast, http://www.crowdcast.com
Prokons, http://www.prokons.com
Table 1. Selected operational prediction markets
Private prediction markets are most pertinent to this discussion. Organizations are
interested in using prediction markets to tap the valuable private information held by
employees and other stakeholders in the organization [21]. Academic references and
analyses on the use of prediction markets as internal decision support tools for various
organizational functions is still limited, although increasing all the time. Ortner [28]
describes the use of prediction markets in a project management process in Siemens in
Austria, with another example of prediction markets use in project management of-
fered by Remidez and Joslin [25]. A number of papers discuss the use of prediction
markets as sales forecasting tools [29], [30]. A similar case study, forecasting market
share in the Austrian mobile phone market is described by Waitz and Mild [31].
Hopman [32] describes the use of prediction markets for demand forecasting in Intel,
with other authors offering examples from the medical domain [24], [33]. Hahn and
Tetlock report Eli Lilly have used prediction markets to evaluate what drugs will be
successful, while Microsoft have used them to forecast sales of software [18]. Other
organizations that are reported in the literature as having used prediction markets
include Motorola, Qualcomm, Infoworld, MGM, Chiron, TNT, EA Games, Yahoo,
Corning, Masterfoods, Pfizers, Abbott, Chrysler, General Mills, O’Reilly and TNT
[34].
Other authors have focused on providing theoretical descriptions of the applica-
tions of prediction markets in organizations. Passmore et al. [35] describe how predic-
tion markets can be used to support the Human Resource function in organizations.
Other authors have suggested prediction markets can have applications in the domain
of risk management [36]–[39]. Sunstein [16] offers a list of possible applications of
prediction markets, while other authors point out the power of prediction markets as
communication tools in an organizational setting [40,41].
3 Open Research Questions
Much of the academic work on prediction markets to date has focused on assessing
their accuracy, both relative to comparable methods and in absolute terms. Academic
research to date suggests that prediction markets “can provide more accurate forecast-
ing and effective aggregation than other predictive technologies” [10, p. 45]. Empiri-
cal work has demonstrated their effectiveness versus competing mechanisms [42].
Other authors caution against drawing definitive conclusions, but summarise the exist-
ing empirical evidence as cautiously optimistic [43]–[45].
The establishment of the basic credibility of prediction markets as information ag-
gregation tools has to lead to calls for studies which move beyond assessing predictive
accuracy [46]. A number of research questions emerge from the literature. Much of
the predictive power of prediction markets is derived from having large numbers of
traders. A key question that emerges from the literature is how traders can be attracted
to participate in prediction markets [45]. Related to this question is the concern that
as a group decision making tool, prediction markets may be more attractive to indi-
viduals who possess certain personality traits. If prediction markets only attract
individuals with a high risk tolerance, this may potentially limit their useful-
ness, particularly in organisational decision making contexts.
Another major concern noted in the literature is the theoretical possibility that pre-
diction markets can be adversely affected by manipulation [10], [45], [47]. In this
context, manipulation is an attempt by an individual or group of traders to affect the
outcome of the prediction market in a manner which contradicts their own privately
held information. Individuals may be motivated to manipulate a prediction market if
their utility for determining the outcome of a prediction market outweighs the incen-
tives offered for truthful information revelation.
4 Proposed Research Agenda
Addressing the research question outline above requires a research methodology
which can provide data which has a number of properties. A research agenda investi-
gating these concerns requires the use of psychometric instruments to measure per-
sonality traits of individuals and the correlation of those measurements with observed
behaviours in a prediction market. It would be necessary to collect data across a tem-
poral period. This would allow gathering data on how trading patterns and behaviours
such as attempted manipulations impact upon the market as a whole. By collecting
data across a temporal window and correlating that data with measurements of indi-
vidual participant’s personalities, it becomes possible to investigate how different
individuals respond to different types of feedback.
One potential source of data that can be used to investigate these issues is predic-
tion markets which are used in a pedagogical setting. This application of prediction
markets has recently begun to receive academic interest [39], [48], [49]. The benefits
of prediction market participation to learners in the cognitive and affective domains of
learning makes a powerful case for their inclusion in curricula, particularly in
large group teaching environments. This in turn opens up the possibility of predic-
tion markets that are run in an educational setting being used as research tools.
A prediction market that is ran as part of a large course will have a stable pool of
participants. The participants can be accessed directly by researchers, and prompted to
complete psychometric instruments. The prediction market will be a relatively con-
trolled environment in that the questions asked, the duration of the trading periods and
the incentives offered are all under the control of the researcher. The data that is col-
lected by the market, including price movements and trading decision are all captured
by the market and can be correlated with data on specific individuals through the use
of an identifier. Of courses it is necessary to ensure that the pedagogical justification
for using prediction markets in an educational design is not undermined by the
research programme, but with careful design it should be possible to both provide
learners with a valuable educational experience and at the same time drive a research
agenda forward.
5 Conclusions
Prediction markets have been positioned in the literature as tool for enabling collec-
tive intelligence and group decision making. Their potential has led to calls in the
literature for more nuanced research programmes which move beyond evaluating their
accuracy to investigate issues such as participant behaviour and the effect of manipu-
lation.
We have pioneered the use of prediction markets as pedagogical tools, and have
published extensively in the area. In this paper, we propose that this specific applica-
tion of prediction market is a potentially useful research methodologies that can be
used in investigate a number of issues of concern to prediction markets researchers.
We believe that we can make a contribution to the larger study of collective intelli-
gence by providing a more nuanced understanding of the strengths, weaknesses
and characteristics of prediction markets. We would welcome collaborators and
partners who would be interested in developing this research agenda. We would be
delighted to offer our expertise in deploying prediction markets in an educational set-
ting to partners, with a view to developing a research agenda that could both investi-
gate the research questions outlined above and also investigate the effect of culture on
prediction market performance.
6 References
[1] G. Tziralis and I. Tatsiopoulos, “Prediction Markets: An Extended Literature Re-
view,” J. Predict. Mark., vol. 1, no. 1, pp. 75–91, Feb. 2007.
[2] F. A. Hayek, “The Use of Knowledge In Society,” Am. Econ. Rev., vol. 35, no. 4, pp.
519–530, 1945.
[3] E. F. Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work,”
J. Finance, vol. 25, no. 2, pp. 383–417, 1970.
[4] G. Kamp and P. A. Koen, “Improving the Idea Screening Process within Organi-
zations using Prediction
Markets: A Theoretical Perspective,” J. Predict. Mark., vol. 3, no. 2, pp. 39–64, Aug. 2009.
[5] R. Kolb, Understanding futures markets, 5th ed. Malden MA: Blackwell Publishers,
1997.
[6] R. Hanson, “Logarithmic Market Scoring Rules for Modular Combinatorial In-
formation Aggregation,” J. Predict. Mark., vol. 1, no. 1, pp. 3–15, Feb. 2007.[7] B.. G.
Malkiel, “The Efficient Market Hypothesis and Its Critics,” J. Econ. Perspect., vol. 17,
no. 1, pp. 59–82, Winter 2003.
[8] B. G. Malkiel, “Reflections on the Efficient Market Hypothesis: 30 Years Later,” Fi-
nanc. Rev., vol. 40, no. 1, pp. 1–9, Feb. 2005.
[9] R. Roll, “Orange Juice and Weather,” Am. Econ. Rev., vol. 74, no. 5, pp. 861–880,
1984.
[10] C. Hall, “Prediction Markets: Issues and Applications,” J. Predict. Mark., vol. 4,
no. 1, pp. 27–58, May 2010.
[11] D. G. Ellis and B. A. Fisher, Small Group Decision Making: Communication and the
Group Process, 4th ed. New York: McGraw-Hill Book Company, 1994.
[12] T. W. Bell, “Private Prediction Markets and the Law,” J. Predict. Mark., vol. 3,
no. 1, pp. 89–110, Apr. 2009.
[13] J. Wolfers and E. Zitzewitz, “Prediction markets,” J. Econ. Perspect., vol. 18, no. 2,
pp. 107–126, 2004.
[14] E. Servan-Schreiber, J. Wolfers, D. M. Pennock, and B. Galebach, “Prediction
Markets: Does Money Matter?,” Electron. Mark., vol. 14, no. 3, pp. 243–251, 2004.
[15] R. W. Hahn and P. C. Tetlock, “Introduction to Information Markets,” in Infor-
mation Markets: A New Way of Making Decisions, Washington D.C: AEI-Brookings
Joint Center for Regulatory Studies, 2006, pp. 1–12.
[16] C. R. Sunstein, “Deliberation and Information Markets,” in Information Markets: A
New Way of Making Decisions, Washington D.C: AEI-Brookings Joint Center for Regula-
tory Studies, 2006, pp. 66–100.
[17] J. E. Berg and T. A. Rietz, “Prediction Markets as Decision Support Systems,” Inf.
Syst. Front., vol. 5, no. 1, pp. 79–93, Jan. 2003.
[18] R. W. Hahn and P. C. Tetlock, Information Markets: A New Way of Making Deci-
sions. Washington D.C: AEI-Brookings Joint Center for Regulatory Studies, 2006.
[19] C. R. Sunstein, “Deliberating Groups vs. Prediction Markets (or Hayek’s Chal-
lenge to Habermas),” Episteme J. Soc. Epistemol., vol. 3, no. 3, pp. 192–213, 2006.
[20] K. Kálovcová and A. Ortmann, “Understanding the Plott-Wit-Yang Paradox,” J.
Predict. Mark., vol. 3, no. 3, pp. 33–44, Dec. 2009.
[21] T. S. Gruca and J. E. Berg, “Public Information Bias and Prediction Market Accu-
racy,” J. Predict. Mark., vol. 1, no. 3, pp. 219–231, Dec. 2007.
[22] J. E. Berg and T. A. Rietz, “The Iowa Electronic Markets: Stylized Facts and Open
Issues,” in Information Markets: A New Way of Making Decisions, R. W. Hahn and P. C.
Tetlock, Eds. Washington D.C: AEI- Brookings Joint Center for Regulatory Studies, 2006,
pp. 142–169.
[23] A. Graefe and C. Weinhardt, “Long-Term Forecasting with Prediction Markets A
Field Experiment on Applicability and Expert Confidence,” J. Predict. Mark., vol. 2, no.
2, pp. 71–91, Sep. 2008.
[24] P. M. Polgreen, F. Nelson, and G. Neumann, “Using Prediction Markets to Forecast
Trends in Infectious Diseases,” Microbe, vol. 1, no. 10, pp. 459–465, 2006.
[25] H. Remidez and C. Joslin, “Using Prediction Markets to Support IT Project Manage-
ment - Research in Progress,” in International Research Workshop on IT Project Man-
agement 2007, Montreal, Quebec, Canada, 2007.
[26] P. W. Rhode and K. S. Strumpf, “Historical Presidential Betting Markets,” J. Econ.
Perspect., vol. 18, pp. 127–141, May 2004.
[27] P. W. Rhode and K. S. Strumpf, “Historical Political Futures Markets: An In-
ternational Perspective,” Universtiy of Arizona, Oct-2008.
[28] G. Ortner, “Forecasting Markets - An Industrial Application,” Technical University of
Vienna, 1997.
[29] K.-Y. Chen and C. R. Plott, “Information Aggregation Mechanisms: Concept, De-
sign and Implementation for a Sales Forecasting Problem,” Hewlett Packard Labs, Califor-
nia, 2002.
[30] K.-Y. Chen, L. R. Fine, and B. A. Huberman, “Predicting the Future,” Inf. Syst.
Front., vol. 5, no. 1, pp. 47–61, Jan. 2003.
[31] M. Waitz and A. Mild, “Improving Forecasting Accuracy in Corporate Prediction
Markets A Case Study in the Austrian Mobile Communication Industry,” J. Predict. Mark.,
vol. 3, no. 3, pp. 49–62, Dec. 2009.
[32] J. Hopman, “Using Forecasting Markets to Manage Demand Risk,” Intel Technol.
J., vol. 11, no. 2, pp. 127–136, 2007.
[33] D. Rajakovich and V. Vladimirov, “Prediction Markets as a Medical Forecasting Tool:
Demand for Hospital Services,” J. Predict. Mark., vol. 3, no. 2, pp. 78–106, Aug. 2009.
[34] G. Tziralis, G. Vagenas, and S. Ponis, “Prediction Markets, an Emerging Web 2.0
Business Model: Towards the Competitive Intelligent Enterprise,” in Web 2.0, Boston, MA:
Springer US, 2009, pp. 271–291.[35] D. L. Passmore, E. D. Cebeci, and R. M. Baker,
“Market-Based Information for Decision Support in Human Resource Development,” Hum.
Resour. Dev. Rev., vol. 4, no. 1, pp. 33 –48, Mar. 2005.
[36] O. Bergfjord, “Prediction Markets as a Tool for Management of Political Risk,” J.
Predict. Mark., vol. 2, no. 2, pp. 1–12, Sep. 2008.
[37] M. Berlemann and C. Schmidt, “Predictive Accuracy of Political Stock Markets Em-
pirical Evidence from a European Perspective,” Humboldt Universitat Berlin, 2001.
[38] G. Bruggelambert, “Information and efficiency in political stock markets: using com-
puterized markets to predict election results,” Appl. Econ., vol. 36, no. 7, pp. 753–768,
2004.
[39] J. Garvey and P. Buckley, “Implementing control mutuality using prediction mar-
kets: a new mechanism for risk communication,” J. Risk Res., vol. 13, no. 7, pp. 951–960,
2010.
[40] P. Buckley and F. McGrath, “Managing prediction markets,” in Proceedings of the
special interest group on management information system’s 47th annual conference on
Computer personnel research, New York, NY, USA, 2009, pp. 217–220.
[41] M. Gebauer and D. Glienke, “Communicating Strategy with Information Markets,” in
Analysis, San Diego, CA., 2007.
[42] G. H. Van Bruggen, M. Spann, G. L. Lilien, and B. Skiera, “Prediction Markets as
institutional forecasting support systems,” Decis. Support Syst., vol. 49, no. 4, pp. 404–416,
Nov. 2010.
[43] T. S. Gruca, J. E. Berg, and M. Cipriano, “Incentive and Accuracy Issues in Movie
Prediction Markets,” J. Predict. Mark., vol. 2, no. 1, pp. 29–43, May 2008.
[44] J. Ledyard, “Designing Information Markets for Policy Analysis,” in Information
Markets: A New Way of Making Decisions, Washington D.C: AEI-Brookings Joint Center
for Regulatory Studies, 2006, pp. 37–66.
[45] J. Wolfers and E. Zitzewitz, “Five Open Questions About Prediction Markets,” in In-
formation Markets: A New Way of Making Decisions, Washington D.C: AEI-Brookings
Joint Center for Regulatory Studies, 2006, pp. 13–36.
[46] T. S. Gruca, J. E. Berg, and M. Cipriano, “Consensus and Differences of Opinion
in Electronic Prediction Markets,” Electron. Mark., vol. 15, no. 1, p. 13, 2005.
[47] C. Deck and D. Porter, “Prediction Markets in the Laboratory,” J. Econ. Surv., p. n/a–
n/a, 2013.
[48] P. Buckley, J. Garvey, and F. McGrath, “A case study on using prediction markets
as a rich environment for active learning,” Comput. Educ., vol. 56, no. 2, pp. 418–428,
Feb. 2011.
[49] D. Raban and D. Geifman, “Designing Online Information Aggregation and Predic-
tion Markets for MBA Courses,” Interdiscip. J. E-Learn. Learn. Objects, vol. 5, pp. 247–
262, 2010.