Agent-Based Modeling: Modeling the Complexity of the Marketplace
- September 28, 2012
The early twentieth century author H.L. Mencken insightfully wrote, “For every complex problem there is an answer that is clear, simple, and wrong.” We humans routinely make mistakes due to oversimplification. Most of us instinctively prefer simple over complex, black and white over shades of gray, right or wrong over it depends. We deal with our highly complex, global, super industrial reality using mental equipment that evolved in the simpler world of hunters and gatherers. Our minds are not instinctively equipped to grapple with the very nuanced and interactive complexities of our modern reality. So we look with biased eyes to see simplicity in a world that actually has deep complexity.
Perhaps you’re thinking, “An interesting observation, but what does it have to do with using simulation modeling for effective product design and marketing decisions?” The answer is that those decisions will succeed or fail in a “complex adaptive system,” in this case the modern marketplace. As noted in our last agent-based modeling blog post, the job of marketers is to try to predict the differing probable outcomes of alternative product design and marketing decisions. To successfully predict which of our decisions have the greatest chance for success, we need simulation modeling tools that can realistically model the complexity of the complex social system that is the modern marketplace.
As a method for modeling a complex adaptive system like the modern marketplace, agent-based modeling has four key advantages over other simulation modeling techniques that are more commonly used in market research:
- Employing “bottom up” modeling methods
- Methods that are computational as well as mathematical
- Capable of modeling complex interactivity between marketplace agents
- Able to predict emergent outcomes in the marketplace
Bottom Up Modeling
Most marketing research simulation methods attempt to market the entire marketplace system at the aggregate level. Agent-based modeling, conversely, models the system’s key individual agents individually, letting the aggregate outcome emerge from the decisions and interactions of the agents. For example, each consumer agent in an ABM simulator can be modeled on an actual survey respondent, using survey data that reflects each consumer’s market behavior decisions as they are influenced by:
- Decision rules, that can differ from one consumer agent to another
- Perceptions of different brands
- Word of mouth influences
- Observations of other consumers’ behaviors
- Advertising influences
With agent-based modeling, our model of the marketplace does not have to be distilled to an over-simplified representation of “average” behavior.
Most simulation techniques used in market research, such as choice modeling simulation, rely almost entirely on mathematical algorithms. Agent-based modeling will often employ mathematical algorithms as components, but also makes heavy use of computational methods. Emphasis on mathematical modeling in the market research industry is not due to superiority of mathematical modeling over computational modeling. Effective computational modeling requires a great deal of computational power, and it’s only in recent years that the level of computational power needed to do advanced agent-based modeling has become widely available.
Because agent-based modeling is computational as well as mathematical, it can model market processes with greater flexibility, comprehensiveness, and detail. Each consumer agent in the simulation system:
- Perceives what’s happening in the simulated environment. For example, marketing messages that are programmed as model inputs to reflect the marketing decisions we want to test
- Uses their own programmed decision rules, which can differ from one agent to another, to make the individual decisions that collectively create the marketplace outcome.
Multiple times above we’ve referred to agent-based modeling’s ability to model consumer influences such as word of mouth and advertising information. In the real world marketplace, such influences impact consumers’ buying decisions through their interactions with each other. A key advantage of agent-based modeling is that it can model this process of complex interactivity, due mainly to its heavy use of computational modeling. The ability to model complex interactivity results in an ability to predict potential emergent outcomes. This is important because, in the real world marketplace, all outcomes are emergent outcomes.
The agent-based modeling approach accepts the reality that the marketplace outcomes from marketers’ decisions emerge from the complex interactivity of the marketplace. Appropriately, in an agent-based modeling market simulator, the agents’ interactions can produce, and thereby predict, complex emergent outcomes. Emergent outcomes occur commonly in the real world marketplace, but they cannot be predicted through models that are based purely on mathematical equations.
Agent-based modeling can more realistically simulate and forecast consumers’ marketplace decisions and behaviors because it uses computational methods to model consumer behavior from the “bottom up,” and can therefore simulate the effects of complex interactivity to predict emergent marketplace outcomes. Next week look for our final agent-based modeling blog post, with a summary of how agent-based modeling can be used to integrate more narrowly focused simulation modeling techniques to provide more holistic market simulation tools.
Editor’s Note: This is the third post in a series on the topic of agent-based modeling by Mick McWilliams, LRW’s SVP of Research Methods. You can read the first post in the series which is an introduction to agent-based modeling and the second post in the series on the probabilities of different outcomes.