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A computational consumer-driven market model: statistical properties and the underlying industry dynamics

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Abstract

We present an agent-based market model in which social emulation by consumers and the adaptation of producers to demand play a significant role. Our theoretical approach considers boundedly-rational agents, heterogeneity of agents and product characteristics, and the co-evolution of consumers’ desires and firms’ adaptation efforts. The model reproduces, and allows us to interpret, statistical regularities which have been observed in the evolution of industrial sectors, and that seem to be also significant in the case of discretionary consumption activities. Thus, we suggest new determinants and explanations (from the consumer-side) for these stylized facts, and we obtain new theoretical patterns which may be of help to better understand the dynamics of discretionary goods markets. This model and results may contribute to guide future research on the field of consumer market.

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Notes

  1. Those goods which are not strictly necessary for everyday life.

  2. Clearly these goods are heavily influenced by social emulation, and this could explain the emergence of consumer trends and the diversity of tastes.

  3. The characteristic space is inspired by Lancaster (1966).

  4. In each time period consumers only make one purchase decision but many social interactions can occur, which must take place sequentially in order to be implemented computationally. For this reason, periods are divided into steps and we use an asynchronous–random updating mechanism that sets the order in which consumers interact (see, for example, Miller and Page 2004).

  5. We consider the Euclidean distance, although depending on the specific features of each product, other metrics might be more suitable.

  6. We have considered that representative niches are those with a minimum size equivalent to 5 % of the population; clearly, other thresholds could be set up, but these changes produce no significant variations in the model dynamics.

  7. This parameter can measure two kinds of market opacity. On the demand side, the uncertainty regarding the precise desires of the niche. On the supply side, it shows the possibility of not being able to satisfy exactly the consumer desires, or not knowing how to do it technically.

  8. We are interested in proving that our model can show several well-known industrial patterns, which really requires finding only one specific parameter set that can generate them. In particular, in our simulations we take the default parameter values: N = 75, α = 24, β = 6, p = 0.003, r = 0.3, δ = 0.8, K = 0.1, M = 0.9, σ = 0.1, q = 0.05, λ = 0.003; although similar time evolutions of the system are obtained over a wide range of parameter values. All figures in this section have been obtained with these default parameter values and averaged from 500 replications (for different random seeds).

  9. Characterized by the three aggregate variables: level of adoption \({{\varLambda }}\left( t \right)\), number of producers \(\prod \left( t \right)\) and Herfindahl index H(t).

  10. We are only interested in proving the existence of a phase transition. The specific value of r that produces it, which depends on the values of the other parameters, is irrelevant for our purposes.

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Authors and Affiliations

  1. Department of Economic Analysis: Quantitative Economics, School of Business and Economics, Autonomous University of Madrid (UAM), E-3 205A, Francisco Tomás y Valiente Street, Number 5, 28049, Madrid, Spain

    Carlos M. Fernández-Márquez

  2. Department of Economic Analysis, Floor 3, School of Business and Economics, University of Zaragoza, Gran Vía Street, Number 2, 50005, Saragossa, Spain

    Francisco Fatas-Villafranca

  3. Department of Economic Analysis: Quantitative Economics, School of Business and Economics, Autonomous University of Madrid (UAM), E-3 312, Francisco Tomás y Valiente Street, Number 5, 28049, Madrid, Spain

    Francisco J. Vázquez

Authors
  1. Carlos M. Fernández-Márquez
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  2. Francisco Fatas-Villafranca
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  3. Francisco J. Vázquez
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Corresponding author

Correspondence to Carlos M. Fernández-Márquez.

Appendix: Pseudo-code for replication

Appendix: Pseudo-code for replication

In this section we include a detailed algorithm that describes the specifications of the model in order to make it less ambiguous and reproducible for any researcher.

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Fernández-Márquez, C.M., Fatas-Villafranca, F. & Vázquez, F.J. A computational consumer-driven market model: statistical properties and the underlying industry dynamics. Comput Math Organ Theory 23, 319–346 (2017). https://doi.org/10.1007/s10588-016-9230-4

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