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Platform Competition and Consumer’s Decisions: An ABM Simulation of Pricing with Different Behavioral Rules

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Part of the Studies in Computational Intelligence book series (SCI,volume 990)

Abstract

I use an agent-based model (ABM) to study how consumer’s behavior influences prices and how common simplifying assumptions constraints the explanatory power of classical models. I simulate an ABM in which two multi-sided platforms compete for attracting buyers and sellers. I use as a framework a theoretical market model in which the difference among behavioral rules is overestimated because of the “market covered” assumption that is necessary to make the model tractable. Making realistic assumptions about the adoption process point out that the difference among behavioral rules is not so extreme as theory says. I find that assumptions regarding the spread of information, such as the network topology, are more relevant in accounting for price differences than behavioral rules.

Keywords

  • Boundedly rationality
  • Agent-based models
  • Price competition
  • Multi-sided markets

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  • DOI: 10.1007/978-3-030-75583-6_19
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Notes

  1. 1.

    See, for example, the work of the Nobel prize winner Richard Thaler.

  2. 2.

    In these markets, companies are platforms that coordinate the demands of several consumer groups that need each other in some way, sellers and buyers, users and developers, etc.

  3. 3.

    The parameter \(\delta _{b}\) controls how buyers value the presence of an additional seller. For simplicity’s sake and without loss of generality, I assume that this value is constant and equal for all buyers and sellers, \(\delta _{b}=\delta _{s}=\delta \).

  4. 4.

    Symmetrically for sellers.

  5. 5.

    Both the preferential and the small-world networks were created with the default primitives of the Netlogo NW extension that are based on the Barabási–Albert algorithm and Watts-Strogatz small-world network. Buyers and sellers have separate networks, although both are either preferential or small-world.

  6. 6.

    A period is an iteration in the simulation model.

  7. 7.

    The probability depends on the normalized degree of each node. The higher the degree, the higher the chance of being infected. The degree of each agent is divided by 4. In this way, the most connected node will only be infected in 1 out of 4 cases.

  8. 8.

    The infection process assumes either the innovators are scattered or clustered. The insights remain valid in both cases. The only change is the adoption level that is lower when clustered.

  9. 9.

    See [6] for a full description of the algorithm.

  10. 10.

    Profitability is measured in terms of net profits. In this framework, it is the sum of the revenues on buyers’ and sellers’ sides. Formally: \(n_s * p_{s,j} + n_b * p_{b,j}\).

  11. 11.

    The number of buyers and sellers is arbitrarily selected. Conclusions will not change with a different number of agents.

  12. 12.

    To avoid that different behavioral rules from the sellers’ side may influence the results on buyers’ side, sellers behave as rational agents in all the simulations.

  13. 13.

    Results available upon request.

References

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Correspondence to Juan Manuel Sánchez-Cartas .

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Sánchez-Cartas, J.M. (2021). Platform Competition and Consumer’s Decisions: An ABM Simulation of Pricing with Different Behavioral Rules. In: Bucciarelli, E., Chen, SH., Corchado, J.M., Parra D., J. (eds) Decision Economics: Minds, Machines, and their Society. DECON 2020. Studies in Computational Intelligence, vol 990. Springer, Cham. https://doi.org/10.1007/978-3-030-75583-6_19

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