Skip to main content

Size Effects in Agent-Based Macroeconomic Models: An Initial Investigation

  • Conference paper
  • First Online:
Agent-Based Approaches in Economics and Social Complex Systems IX

Part of the book series: Agent-Based Social Systems ((ABSS,volume 15))

  • 692 Accesses

Abstract

We investigate the scale-free property of an agent-based macroeconomic model initially proposed by Wright (Physica A, 346:589–620, 2005), called the Social Architecture (SA) model. The SA model has been shown to be able to replicate a number of important features of a macroeconomy, such as patterns concerning economic growth, business cycles, industrial dynamics, and income distribution. We explore whether macroeconomic stylized features resulting from this model are robust when the number of agents populating the (model) economy varies. We simulate the model by systematically varying the agent population with 100, 500, 1,000, 2,000, 4,000, 8,000, and 10,000 agents. Our results indicate that the SA model does exhibit significant size effects for several important variables.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The EM principle is also known as the zero-intelligence agent in the literature. However, as Chen (2012) has argued, this term may be misleading since the behavioral assumption may have nothing to do with the cognitive abilities of agents. Therefore, we prefer using a different and a more formal term.

  2. 2.

    For a survey of the use of the entropy-maximization principle in the agent-based modeling of economics and finance, the interested reader is referred to Ladley (2012).

  3. 3.

    The simulations are performed using both MATLAB and NetLogo, and the codes are publicly available on the Internet (see Sect. 3).

  4. 4.

    However, since the status of being an employee or an employer can change over time, a directed graph is more convenient.

  5. 5.

    Notice that, since the amount of money is initially randomly distributed among all agents, we can only fix the money holding per capita, but not the possible distribution effect.

  6. 6.

    Since we compare distributions across different sizes of the economy, we normalize the absolute value of the number of firm demises into ratios with respect to the size of the economy.

  7. 7.

    See Wright (2005), p. 598, for the rationale behind doing so.

  8. 8.

    For the less stringent case of p = 0. 05, a few combinations also exhibit size effects in addition to those in Table 3. The variables and the corresponding combinations are worker ratio (100/2,000, 100/8,000, 4,000/10,000, 8,000/10,000), firm demise ratio (1,000/2,000), wealth Gini (500/1,000), yearly wage share (8,000/10,000), firm growth (employment) (500/1,000, 500/2,000), rate of profit (100/500, 100/2,000, 500/2,000, 2,000/4,000), and recession yearly (4,000/10,000). However, the overall patterns concerning the size effects remain unchanged.

References

  • Barabasi, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.

    Article  Google Scholar 

  • Challet, D., & Marsili, M. (2003). Criticality and market efficiency in a simple realistic model of the stock market. Physical Review, 68(3), 036132-1–036132-4.

    Google Scholar 

  • Chen, S.-H. (2012). Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective. Journal of Economic Dynamics and Control, 36(1), 1–25.

    Article  Google Scholar 

  • Duffy, J., & Unver, M. (2006). Asset price bubbles and crashes with near zero-intelligence traders. Economic Theory, 27, 537–563.

    Article  Google Scholar 

  • Egenter, E., Lux, T., & Stauffer, D. (1999). Finite-size effects in Monte Carlo simulations of two stock market models. Physica A: Statistical Mechanics and Its Applications, 268(1), 250–256.

    Article  Google Scholar 

  • Farmer, D., Patelli, P., & Zovko, I. (2005). The predictive power of zero intelligence in financial markets. Proceedings of the National Academy of Sciences, 102, 2254–2259.

    Google Scholar 

  • Gode, D., & Sunder, S. (1993). Allocative efficiency of markets with zero intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy, 101, 119–137.

    Article  Google Scholar 

  • Ladley, D. (2012). Zero intelligence in economics and finance. Knowledge Engineering Review, 27(2), 273–286.

    Article  Google Scholar 

  • Lavicka, H., & Novotny, J. (2013). Employment, production and consumption with random update: Non-equilibrium stationary state equations. Acta Polytechnica, 53(6), 847–853.

    Article  Google Scholar 

  • LeBaron, B., & Tesfatsion, L. (2008). Modeling macroeconomies as open-ended dynamic systems of interacting agents. American Economic Review, 98(2), 246–250.

    Article  Google Scholar 

  • Lux, T., & Schornstein, S. (2005). Genetic learning as an explanation of stylized facts of foreign exchange markets. Journal of Mathematical Economics, 41(1–2), 169–196.

    Article  Google Scholar 

  • Sunder, S. (2004). Market as artifact: Aggregate efficiency from zero-intelligence traders. In M. Augier & J. March (Eds.), Models of a man: Essays in memory of Herbert A. Simon (pp. 501–519). Cambridge: MIT Press.

    Google Scholar 

  • Wright, I. (2005). The social architecture of capitalism. Physica A, 346, 589–620.

    Article  Google Scholar 

  • Wright, I. (2009). Implicit microfoundations for macroeconomics. Economics: The Open-Access, Open-Assessment E-Journal, 3, 1–27.

    Google Scholar 

Download references

Acknowledgements

The first and the last authors are grateful for the research support in the form of the Ministry of Science and Technology (MOST) Grants, MOST 103-2410-H-004-009-MY3 and MOST 104-2811-H-004-003, respectively.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shu-Heng Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Chen, SH., Kao, YF., Chie, BT., Meyer, T., Venkatachalam, R. (2017). Size Effects in Agent-Based Macroeconomic Models: An Initial Investigation. In: Putro, U., Ichikawa, M., Siallagan, M. (eds) Agent-Based Approaches in Economics and Social Complex Systems IX. Agent-Based Social Systems, vol 15. Springer, Singapore. https://doi.org/10.1007/978-981-10-3662-0_11

Download citation

Publish with us

Policies and ethics