Abstract
Heterogeneity in beliefs in financial markets constitutes a major obstacle to coordination. Beliefs being heavily driven by information, efficient coordination and selection of information in financial markets are interconnected matters. The dialectic between different mechanisms at play, behavioral, cognitive, and interactive, has received little investigation. However, it appears fundamental in opening the black box of agents’ decision-making in the context of information selection. We introduce an agent-based model of information signal selection. In line with the Agent Zero neurocognitive-founded model (Epstein in Agent zero: toward neurocognitive foundations for generative social science. Princeton University Press 2014), agents are endowed with deliberative, affective, and social modules, through which they acquire disposition to adopt a signal. Agents receive signal values and estimate their variance with memory, i.e., ability to remember past signal values; they emotionally react to shocks and transmit individual dispositions through social networks. Information selection efficiency and opinion volatility are studied activating different combinations of cognitive modules and considering various network topologies. Human cognition is significantly more complex. Yet, this simple model generates a rich set of behaviors, and complex general mechanisms of information selection emerge. Simulation results obtained with novel model exploration methodologies outline that the impact of network structure and of memory abilities on market coordination, information selection efficiency and opinion stability appear to vary in magnitude and in sign depending on the combinations of cognitive modules considered. The behavior of the aggregate cognition framework appears to be different from the aggregate behavior of its modules taken separately, and interactions between cognitive modules appear crucial to explain information selection at the micro-level.
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References
Akerlof GA, Shiller RJ (2010) Animal spirits: How human psychology drives the economy, and why it matters for global capitalism. Princeton University Press
Allen F, Morris S, Shin HS (2006) Beauty contests and iterated expectations in asset markets. Oxford University Press, Oxford
Amblard F, Deffuant G (2004) The role of network topology on extremism propagation with the relative agreement opinion dynamics. Phys Stat Mech Appl 343:725–738
Arthur BW, Holland JH, Lebaron B, Palmer RG, Tayler P (1996) Asset pricing under endogenous expectations in an artificial stock market
Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512
Bernardo JM, Smith AF (2009) Bayesian theory, vol. 405. Wiley
Bracha A, Weber EU (2012) A psychological perspective of financial panic. FRB of Boston Public Policy Discussion Paper (12-7)
Branch WA, McGough B (2009) A New Keynesian model with heterogeneous expectations. J Econ Dyn Control 33(5):1036–1051
Brock WA, Hommes CH (1999) Rational animal spirits. In: Herings PJJ, van der Laan G, Talman AJJ (eds) The theory of markets, North-Holland, Amsterdam, pp 109–137
Brock WA, Hommes CH (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. J Econ Dyn Control 22(8–9):1235–1274
Chérel G, Cottineau C, Reuillon R (2015) Beyond corroboration: Strengthening model validation by looking for unexpected patterns. PLOS ONE 10(9)
Chiarella C, He X (2001) Asset price and wealth dynamics under heterogeneous expectations. Quant Financ 1(5):509–526
Dosi G, Napoletano M, Roventini A, Stiglitz J, Treibich T (2017) Rational Heuristics? Expectations and behaviors in evolving economies with heterogeneous interacting agents
Epstein JM (2014) Agent zero: Toward neurocognitive foundations for generative social science, vol 25. Princeton University Press
Erdos P, Rényi A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5(1):17–60
Frank H (2003) Natural selection, rational economic behavior and alternative outcomes of the evolutionary process. J Socio-Econ 32–6(12):601–622
Friedkin NE, Johnsen EC (1990) Social influence and opinions. J Math Sociol 15(3–4):193–206
Gaunersdorfer A (2000) Endogenous fluctuations in a simple asset pricing model with heterogeneous agents. J Econ Dyn Control 24:799–831
Hommes C (2001) Financial markets as nonlinear adaptive evolutionary systems
Hommes C (2006) Heterogeneous agent models in economics and finance. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics, vol 2, Chap 23. Elsevier, pp 1109–1186
Hommes C (2013) Behavioral rationality and heterogeneous expectations in complex economic systems. Cambridge University Press
Kirman A (1991) Epidemics of opinion and speculative bubbles in financial markets. In: Taylor M (ed) Money and financial markets. Blackwell, Oxford, pp 354–368
Kirman A (2010) Complex economics: individual and collective rationality. Routledge
Kristoufek L, Vosvrda M (2018) Herding, minority game, market clearing and efficient markets in a simple spin model framework. Commun Nonlinear Sci Numer Simul
Loewenstein G (2000) Emotions in economic theory and economic behavior. Am Econ Rev 90(2):426–432
Lux T, Marchesi M (1999) Scaling and criticality in a stochastic multiagent model of a financial market. Nature 397:498–500
Mauersberger F, Nagel R (2018) Levels of reasoning in Keynesian beauty contests: a generative framework. In: Handbook of computational economics, vol 4, 10, pp 541–634
Michard Q, Bouchaud JP (2005) Theory of collective opinion shifts: from smooth trends to abrupt swings. Eur Phys J B-Condensed Matter Complex Syst 47(1):151–159
Orléan A (1995) Bayesian interactions and collective dynamics of opinion: Herd behavior and mimetic contagion. J Econ Behav Organ 28(2):257–274
Reinhart CM, Rogoff KS (2009) This time is different: Eight centuries of financial folly. Princeton University Press
Reuillon R, Chuffart F, Leclaire M, Faure T, Dumoulin N, Hill DRC (2010) Declarative task delegation in OpenMOLE. In: Proceedings of high performance computing and simulation (HPCS) international conference
Reuillon R, Leclaire M, Rey-Coyrehourcq M (2013) OpenMOLE, a workflow engine specifically tailored for the distributed exploration of simulation models. Future Gener Comput Syst
Vié, A (2018) Selecting information in financial markets herding and opinion swings in a heterogeneous mimetic rational agent-based model. In: International conference on complex systems. Springer, Cham, pp 120–128
Vié A, Morales AJ (2019) How connected is too connected? Impact of network connectivity on systemic risk and collapse of complex economic systems. In press
Wildemeersch M, Strelkovskii N, Poledna S, Leduc M (2016) Network resilience and systemic risk. In: European meetings on cybernetics and systems research, methodological approaches to address network resilience
Wilensky U (1999). NetLogo. Center for connected learning and computer-based modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/
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Vié, A. (2019). Information Selection Efficiency in Networks: A Neurocognitive-Founded Agent-Based Model. In: Chakrabarti, A., Pichl, L., Kaizoji, T. (eds) Network Theory and Agent-Based Modeling in Economics and Finance. Springer, Singapore. https://doi.org/10.1007/978-981-13-8319-9_2
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DOI: https://doi.org/10.1007/978-981-13-8319-9_2
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