1. Simulation Modeling in Organizational and Management Research Ludovico Prattico Academic Paper Summary Technology Innovation Management June 11, 2009

2. June 11, 2009lprattic@connect.carleton.caSlide 2 Preamble Paper reviewed: –Harrison, J.R., Lin, Z., Carroll, G.R., Carley, K.M. 2007. Simulation modeling in organizational and management research. Academy of Management Journal, (32)4:1229-1245 Objectives –“Promote understanding of simulation methodology” –“Develop an appreciation of its potential contributions to management theory” Deliverables –Research uses of simulation –Key issues with the use of simulation Relevance –Clearly targets management science researchers –Cites prior work by Axelrod (1997) to illustrate the advantages –Light discussion on the advances made in mathematics and physics due to simulation Contribution –Four new uses of simulation in management theory research

3. June 11, 2009lprattic@connect.carleton.caSlide 3 Background: making scientific progress Two historical methodologies: –theoretical analysis or deduction assumptions are formulated and consequences deduced typically these are mathematical relationships and their consequences are mathematical proofs plenty of examples in physics and mathematics does not lend itself well to management research because assumptions such as perfect rationality, unlimited budgets, etc. have to be made –empirical analysis or induction researches observe the data (variables) analyse the observations to uncover the relationships plenty of examples in science not suitable for management research because it is difficult to observe secret agreements A third method is now recognised: SIMULATION

4. June 11, 2009lprattic@connect.carleton.caSlide 4 Simulation in science Mostly mathematical relationships –Easily handled computationally First documented computer simulations were for the Manhattan Project Computer simulation is now recognised as a way of doing science (Axelrod, 1997; Waldrop 1992) Simulation has taken a backseat in the social sciences likely because of insufficient training Some work was done in the 80s but nothing appeared regularly till 90s An assessment of journal papers publish between 1994-2003 found 8% of the published papers used simulation

5. June 11, 2009lprattic@connect.carleton.caSlide 5 Computer simulations Model the behaviour of a system through the use of mathematical equations and transformation rules –Formal Modelling Formulation of relationships between variables and how the variables change over time Based on theoretical reasoning Focus is on organisational processes –Use formal models –Discrete time designs What happens at time t+1 given what happened at time t Varying initial conditions –Stochastic or deterministic models

6. June 11, 2009lprattic@connect.carleton.caSlide 6 Computer simulations … continued Stochastic models –Probability based –Single draws do not produce representative outcomes –By repeating the simulation a large number of times, the output values have distributions that approach the model behaviour –Typically use is the Monte Carlo Methods Deterministic models –More computational in nature –Run once and always produce the same output given the same initial conditions

7. June 11, 2009lprattic@connect.carleton.caSlide 7 Types of Simulations Agent-based models –Focus on modelling the behaviours of adaptive actors who influence one another through their interactions –These models focus on how the agents influence one another and the consequences of the agent behaviours for the social system as a whole System dynamics models –Focus on modelling the behaviour of the system as whole –These models simulate the processes that lead to changes in the system over time Cellular automata models –Models are based on an n X n lattice or grid with each square representing a cell –Model focuses on how each cell changes from being occupied or not in each time period, as a function of the characteristics of the neighbouring cells

8. June 11, 2009lprattic@connect.carleton.caSlide 8 Uses of Simulation … proposed by Axelrod (1997) Prediction –Observation and analysis of the output variables can provide an understanding of the underlying (unobservable) processes Proof –A simulation can show that it is possible for the modeled process to produce certain types of behaviour Discovery –Lead to the discovery of unexpected consequences of the interaction of the processes defined in the model

9. June 11, 2009lprattic@connect.carleton.caSlide 9 Uses of Simulation … proposed by Harrison, et al (2007) Explanation –Underlying processes can be postulated and if the simulation outcomes fit with the observed behaviours, the postulated processes are a plausible explanation of the behaviours Critique –Similar to Explanation –Except simulation is used to assess pre-existing explanations and to possibly find simpler explanations –“… explore more parsimonious explanations for these phenomena …” Prescription –A simulation may suggest a better method of operation or organising Empirical guidance –Generate new empirical strategies Uncovering systematic connections amongst previously unconnected variables Observing nonlinear relationships indicating inappropriateness of standard statistical testing

10. June 11, 2009lprattic@connect.carleton.caSlide 10 Issues in using simulation Complexity in simulation models –By adding variables or processes, a model can be made more realistic Makes it difficult to understand what drives the results –Suggestion is to use a building block approach to adding new variables/processes Empirical grounding of simulations: How does it relate to the real world? –Model is based on empirical work –When empirical estimates are not available, empirical work can still provide information to construct the model and sensitivity analysis can be used to examine model robustness –Simulation results can be compared to empirical work –Simulations can be used to explore the outcomes of theoretically derived processes providing a form of discovery Problems and limitations –Poor presentation of the model – insufficient detail –Insufficient analysis to demonstrate the implied relationships –Poor translation of the formal model –Coding errors –Concern for the conclusions drawn from the simulations Findings are only valid for the values of the parameters examined and cannot be generalised

11. June 11, 2009lprattic@connect.carleton.caSlide 11 My 2 ¢ Very intense paper –A very good read –Lots of good introductory information –Good guidance as to how simulation of the non-computational variety can be applied –Makes a good case for its roots –Some repetition Lacking –Mention of Monte Carlo Methods was made My belief is these methods are significant in management research Could have used an example, even trivial ones to stimulate the further interest of the reader –Buffon’s needle to calculate 

12. June 11, 2009lprattic@connect.carleton.caSlide 12 … and now the rant We have seen the term parsimonious used a few times now – what do they really mean by it? Miriam-Webster defines it as: –adjective: exhibiting or marked by parsimony; especially frugal to the point of stinginess. Concise Oxford Dictionary defines it as an adjective and references parsimony: noun: 1. carelessness in the use of money or other resources 2. meanness, stinginess 3. law of parsimony: the assertion that no more causes or forces should be assumed than are necessary to account for the facts I never went to law school!

13. June 11, 2009lprattic@connect.carleton.caSlide 13 References Axelrod, R. 1997. The complexity of cooperation: Agent-based models of competition and collaboration. Princeton, NJ: Princeton University Press. Concise Oxford Dictionary. 1990. Oxford, UK: Clarendon Press,. Harrison, J.R., Lin, Z., Carroll, G.R., Carley, K.M. 2007. Simulation modeling in organizational and management research. Academy of Management Journal, (32)4:1229-1245 Merriam-Webster Dictionary – 10 th edition. 1999. Springfield, MA, USA: Merriam-Webster, Inc. Waldrop, M. M. 1992. Complexity: The emerging science at the edge of order and chaos. New York: Simon and Schuster.