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Agent-based simulations: Why, when and how?

ERIM PhD Presentation Series Thursday, 13 September, T3-13, 12.00-12.45


on 26 June 2017

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Transcript of Agent-based simulations: Why, when and how?

Murat Tarakci

Agent-based simulations:
Why, when and how?

Agent-based simulations (ABS)
is "a method for using computer software to model the operation of real-world processes, systems, or events" coupled with an experimental design.
A computational model
Assumptions to simplify the real world
Rules linking the constructs with the simplified world
Emergent results!
(Davis, Eisenhardt & Bingham, 2007, AMR)
Power structures and adaptation:How to distribute power within a group
(Best PhD paper finalist at Strategic Management Society conference)
Do groups with low power disparity (i.e., where power is evenly distributed) outperform groups with high power disparity (i.e., where power is concentrated in only one member)?
Why ABS?
Nature of organizational phenomena
Unobserved heterogeneity
Complex interactions of multiple independent processes
Availability of data, mathematically intractable
Incomplete theories
Why not ABS?
Reviewers' lack of interest and appreciation
Lack of background to conduct or evaluate ABS
External validity
How to conduct ABS
Have a clear research question and purpose
Is the question novel, interesting, and theoretically and practically influential?
What is the existing state of the theory?
Do you need agent-based simulations? (e.g., lack of data, complex interactions, etc.)
(Berg, 2003, AMJ; Rynes, 2002, AMJ)
Select a suitable simulation method from existing platforms and techniques
Platforms: Matlab, NetLogo, Repast, Java, C++, ...
Tecniques: Systems dynamics ,
NK modeling , Cellular
automata , other appropriate
(Repenning, 2002, Org.Sci.)
(Levinthal, 1997, Man.Sci.)
(Lomi & Larsen, 1997, AMJ)
Construct the model
KISS principle
Be transparent!
Check the reliability
Model grounding and validation
Experiment and vary
Initial conditions, time, outcomes, iterations, and variations.
Represent the results in an intuitive way
Be cautious of statistical tests
(Harrison et al., 2007, AMR)
How to conduct ABS
Available online: http://goo.gl/pVlt5
Burton RM. 2003. Computational laboratories for organization science: Questions, validity and
docking. Computational & Mathematical Organization Theory 9 (2): 91-108.
Burton RM, Obel B. 2011. Computational modeling for what-is, what-might-be, and what-
should-be studies-and triangulation. Organization Science 22 (5): 1195-1202.
Burton RM, Obel B. 1995. The validity of computational models in organization science: From
model realism to purpose of the model. Computational & Mathematical Organization
Theory 1 (1): 57-71.
Davis JP, Eisenhardt KM, Bingham CB. 2007. Developing theory through simulation methods.
Academy of Management Review 32 (2): 480-499.
Ganco M, Hoetker G. 2009. NK modeling methodology in the strategy literature: Bounded
search on a rugged landscape. In Research Methodology in Strategy and Management,
Bergh D and Ketchen D (eds). Emerald Group Publishing Ltd; 237-268
Harrison JR, Lin Z, Carroll GR, Carley KM. 2007. Simulation modeling in organizational and
management research. Academy of Management Review 32 (4): 1229-1245.
Some Reference Articles
For what:
What-is, What-might-be, What-should-be
"[They] are tools of research, not ends unto themselves"
(Harrison et al., 2007, AMR)
Robustness of your model
Sensitivity analysis
(Burton & Obel, 2011, Org.Sci.)
Got more questions?
feel free to contact:
Common research methodology:
Theoretical analysis (deduction)
Empirical analysis (induction)
Hypothetico-deductive (HD)
Ketokivi & Mantere, 2010, AMR)
Full transcript