Large-Scale Agent-Based Models in Marketing Research: The Quest for the Mythical Free Lunch
Positioned as an alternative to equation-based methods, agent-based modelling (ABM) has shown notable promise in dealing with cases where the latter has proven inadequate. One of the areas where the limitations of traditional approaches are most pronounced is that of consumer behaviour research. A primary trait encountered within this scope is that of adaptability, and agent-based methods appear to be ideally suited to the task of capturing this dimension. It therefore follows that marketing researchers are likely to gain novel and extensive insight by way of constructing large-scale, complex ABMs. However, the computational cost of complex simulations can be prohibitive. Furthermore, the literature makes little effort to elucidate means of making such ABMs feasible, beyond relying on natural hardware evolution. Unfortunately, the latter source of growth has grown stagnant, and the only avenue for the continued expansion of performance appears to be the move to parallel platforms and programming. The present research presents a cross-section of the current state-of-the-art in high-performance ABM frameworks, and proposes a novel approach to levering the as of yet untapped potential of cheap, ubiquitous Graphics Processing Units (GPUs). This insight is mapped into the space of consumer behaviour research, and a consistent argument is made in favour of larger, more detailed, ABMs, both as alternatives to current approaches as well as a development of prior forays into this area. In conclusion, a call to action is formulated, both to marketing researchers as well as computational economists, emphasizing the interdisciplinary requirements of ABM usage in the field of marketing.
Large-Scale Agent-Based Models in Marketing Research: The Quest for the Mythical Free Lunch, International Journal of Business and Economics Research.
Vol. 2, No. 3,
2013, pp. 33-40.
J. H. Holland, "Complex adaptive systems," Daedalus, vol. 121, no. 1, pp. 17–30, 1992.
K. J. Arrow and G. Debreu, "Existence of an equilibrium for a competitive economy," Econ. J. Econ. Soc., pp. 265–290, 1954.
W. T. Bielby and R. M. Hauser, "Structural equation models," Annu. Rev. Sociol., vol. 3, pp. 137–161, 1977.
J. W. Forrester, Industrial dynamics, vol. 2. MIT press Cambridge, MA, 1961.
M. Buchanan, "Economics: meltdown modelling," Nature, vol. 460, no. 7256, pp. 680–682, 2009.
J. D. Farmer and D. Foley, "The economy needs agent-based modelling," Nature, vol. 460, no. 7256, pp. 685–686, 2009.
D. Colander, M. Goldberg, A. Haas, K. Juselius, A. Kirman, T. Lux, and B. Sloth, "The Financial Crisis and the Systemic Failure of the Economics Profession," Crit. Rev., vol. 21, no. 2–3, pp. 249–267, 2009.
D. Colander, "The Failure of Economists to Account for Complexity," Causes of the Crisis, 12-Sep-2009. .
G. E. Moore, Cramming more components onto integrated circuits. McGraw-Hill, 1965.
W. Rand and R. T. Rust, "Agent-based modeling in marketing: Guidelines for rigor," Int. J. Res. Mark., vol. 28, no. 3, pp. 181–193, 2011.
R. Axtell, "Why agents? On the varied motivations for agent computing in the social sciences," 2000.
K. L. Judd, "Chapter 17 Computationally Intensive Analyses in Economics," in in Handbook of Computational Economics, vol. Volume 2, Elsevier, 2006, pp. 881–893.
"NetLogo User Manual (version 5.0.1)."[Online]. Available: http://ccl.northwestern.edu/netlogo/faq.html.[Accessed: 25-Jun-2012].
S. Luke, C. Cioffi-Revilla, L. Panait, K. Sullivan, and G. Balan, "MASON: A Multiagent Simulation Environment," SIMULATION, vol. 81, no. 7, pp. 517–527, Jul. 2005.
M. J. North, T. R. Howe, N. T. Collier, and J. R. Vos, "The repast simphony runtime system," in Proceedings of the Agent 2005 conference on generative social processes models and mechanisms, 2005, pp. 151–158.
J. L. Hennessy and D. A. Patterson, Computer Architecture: A Quantitative Approach. Elsevier, 2011.
H. Sutter, "The free lunch is over: A fundamental turn toward concurrency in software," Dr Dobb’s J., vol. 30, no. 3, pp. 202–210, 2005.
"Welcome to the Jungle," Sutter’s Mill.[Online]. Available: http://herbsutter.com/welcome-to-the-jungle/.[Accessed: 09-May-2013].
A. R. Brodtkorb, C. Dyken, T. R. Hagen, J. M. Hjelmervik, and O. O. Storaasli, "State-of-the-art in heterogeneous computing," Sci. Program., vol. 18, no. 1, pp. 1–33, Mar. 2010.
J. J. Dongarra and A. J. van der Steen, "High-Performance Computing Systems: Status and Outlook," Acta Numer., vol. 21, pp. 379–474, 2012.
J. M. Epstein and R. L. Axtell, Growing Artificial Societies: Social Science from the Bottom Up, First Edition. A Bradford Book, 1996.
T. C. Schelling, Micromotives and macrobehavior. WW Norton & Company, 2006.
G. A. Akerlof, "The market for‘ lemons’: Quality uncertainty and the market mechanism," Q. J. Econ., pp. 488–500, 1970.
D. K. Gode and S. Sunder, "Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality," J. Polit. Econ., pp. 119–137, 1993.
N. J. Vriend, "Self-organization of markets: An example of a computational approach," Comput. Econ., vol. 8, no. 3, pp. 205–231, 1995.
M. Janssen and W. Jager, "An integrated approach to simulating behavioural processes: A case study of the lock-in of consumption patterns," J. Artif. Soc. Soc. Simul., vol. 2, no. 2, pp. 1–29, 1999.
S. S. Izquierdo and L. R. Izquierdo, "The impact of quality uncertainty without asymmetric information on market efficiency," J. Bus. Res., vol. 60, no. 8, pp. 858–867, 2007.
L. Tesfatsion and K. L. Judd, Handbook of Computational Economics: Agent-Based Computational Economics. Elsevier, 2006.
T. Brenner, "Agent learning representation: Advice on modelling economic learning," Handb. Comput. Econ., vol. 2, pp. 895–947, 2006.
N. J. Vriend, "ACE models of endogenous interactions," Handb. Comput. Econ., vol. 2, pp. 1047–1079, 2006.
R. Marks, "Market design using agent-based models," Handb. Comput. Econ., vol. 2, pp. 1339–1380, 2006.
C. Deissenberg, S. van der Hoog, and H. Dawid, "EURACE: A massively parallel agent-based model of the European economy," Appl. Math. Comput., vol. 204, no. 2, pp. 541–552, Oct. 2008.
V. Pallipuram, M. Bhuiyan, and M. Smith, "A comparative study of GPU programming models and architectures using neural networks," J. Supercomput., pp. 1–46.
B. R. Gaster and L. Howes, "FUNDAMENTAL PROBLEMS," 2012.
AMD, "AMD Graphics Core Next (GCN) Architecture." AMD, 28-Jun-2012.
M. J. Flynn, "Some Computer Organizations and Their Effectiveness," Ieee Trans. Comput., vol. C–21, no. 9, pp. 948–960, 1972.
A. Habermaier and A. Knapp, "On the correctness of the SIMT execution model of GPUs," Program. Lang. Syst., pp. 316–335, 2012.
I. Buck, "GPU computing with NVIDIA CUDA," in ACM SIGGRAPH 2007 courses, New York, NY, USA, 2007.
B. Gaster, D. R. Kaeli, L. Howes, P. Mistry, and D. Schaa, Heterogeneous Computing with OpenCL. Morgan Kaufmann, 2011.
K. Gregory and A. Miller, C++ AMP: Accelerated Massive Parallelism with Microsoft Visual C++. Microsoft Press, 2012.
M. Lysenko and R. M. D’Souza, "A framework for megascale agent based model simulations on graphics processing units," J. Artif. Soc. Soc. Simul., vol. 11, no. 4, p. 10, 2008.
K. S. Perumalla and B. G. Aaby, "Data parallel execution challenges and runtime performance of agent simulations on GPUs," in Proceedings of the 2008 Spring simulation multiconference, San Diego, CA, USA, 2008, pp. 116–123.
P. Richmond and D. M. Romano, "A high performance framework for agent based pedestrian dynamics on gpu hardware," Proc. Eurosis Esm, 2008.
J. Shopf, J. Barczak, C. Oat, and N. Tatarchuk, "March of the Froblins: simulation and rendering massive crowds of intelligent and detailed creatures on GPU," in ACM SIGGRAPH 2008 classes, New York, NY, USA, 2008, pp. 52–101.
H. Li, A. Kolpas, L. Petzold, and J. Moehlis, "Parallel simulation for a fish schooling model on a general-purpose graphics processing unit," Concurr. Comput. Pr. Exp., vol. 21, no. 6, pp. 725–737, 2009.
U. Erra, B. Frola, V. Scarano, and I. Couzin, "An Efficient GPU Implementation for Large Scale Individual-Based Simulation of Collective Behavior," in International Workshop on High Performance Computational Systems Biology, 2009. HIBI ’09, 2009, pp. 51–58.
E. B. Passos, M. Joselli, M. Zamith, E. W. G. Clua, A. Montenegro, A. Conci, and B. Feijo, "A bidimensional data structure and spatial optimization for supermassive crowd simulation on GPU," Comput Entertain, vol. 7, no. 4, pp. 60:1–60:15, Jan. 2010.
A. R. D. Silva, W. S. Lages, and L. Chaimowicz, "Boids that see: Using self-occlusion for simulating large groups on GPUs," Comput Entertain, vol. 7, no. 4, pp. 51:1–51:20, Jan. 2010.
B. G. Aaby, K. S. Perumalla, and S. K. Seal, "Efficient simulation of agent-based models on multi-GPU and multi-core clusters," in Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques, ICST, Brussels, Belgium, Belgium, 2010, pp. 29:1–29:10.
W. Tang and D. A. Bennett, "Parallel agent-based modeling of spatial opinion diffusion accelerated using graphics processing units," Ecol. Model., vol. 222, no. 19, pp. 3605–3615, Oct. 2011.