Abstract
In this paper, we extend the spatial agent-based prediction market proposed by Yu and Chen at MABS 2011 into a spatial model in which agents choose their community (neighbors) by following Schelling’s proximity model. This extended model generalizes the spatial configuration of the original model and enables us to examine the validity of the Hayek hypothesis when the information distribution is determined by clusters of agents with heterogeneous identities. Specifically, we examine the role of the toleration capacity, the key parameter in the Schelling model, which generates the clusters of agents with different sizes, and the role of exploration capacity which determines how well an agent is informed about his local surroundings. We find that after taking into account market activity and price volatility, both the toleration capacity and exploration capacity have a positive effect on the prediction accuracy and enhance information polling and the information aggregation of markets. The results obtained in this agent-based simulation, therefore, add a qualification to the well-known Hayek hypothesis and point to the significance of individuals in information aggregation.
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Notes
- 1.
These parameter values are based on the 2012 Presidential Election in Taiwan. Based on the 2012 Presidential election outcome, the DPP candidate (colored in green) won a share of 45.63Â % of the vote, the KMT candidate (colored in blue) won a share of 51.60Â %, and the PFP candidate (colored in orange) won a share of 2.77Â %.
- 2.
- 3.
Taiwan’s population density is around 630 people per square kilometer. If we only consider the number of qualified voters, and not the entire population size, then the population density is approximately 372 per square kilometer. By assuming that one square kilometer is roughly equal to 32 \(\times \) 32 grids, we can then figure out the required \(d\) (36.12 %) and the number of grids (\(193\times 193\)).
- 4.
We assume that the non-arbitrage condition is always satisfied, i.e.,
$$\begin{aligned} \sum _{j=1}^{3} \bar{p}_{j,l} \times 100 =100, \forall l \end{aligned}$$(10)However, if the above equality is violated, then we shall rescale our mean price as follows,
$$\begin{aligned} \bar{p}_{j,l}^{adj} = \frac{\bar{p}_{j,l}}{\sum _{j = 1}^3 \bar{p}_{j,l}} \times 100, \end{aligned}$$(11)and use the re-scaled price \(\bar{p}_{j,l}^{adj}\) to replace \(\bar{p}_{j,l}\) in Eq. (8).
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Acknowledgments
The authors are grateful to the two anonymous referee reports for their quite careful review of the papers. The paper has been revised by following many of the suggestions made in their painstakingly written reports. The remaining errors are, of course, solely the authors’ responsibilities. The NSC grant NSC 101-2410-H-004-010-MY2 is also gratefully acknowledged.
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Chie, BT., Chen, SH. (2015). Spatial Modeling of Agent-Based Prediction Markets: Role of Individuals. In: Grimaldo, F., Norling, E. (eds) Multi-Agent-Based Simulation XV. MABS 2014. Lecture Notes in Computer Science(), vol 9002. Springer, Cham. https://doi.org/10.1007/978-3-319-14627-0_14
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