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Agent-based modeling and competitive intelligence
Agent-based modeling (ABM) techniques have great potential for use in business, particularly in the competitive intelligence function. This compilation of articles and associated LinkedIn group focuses on the discussion of how ABM techniques may be applied in the business field and in CI specifically.
Curated by Jess Laventall
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Recommended read: Turtles, Termites, and Traffic Jams

Recommended read: Turtles, Termites, and Traffic Jams | Agent-based modeling and competitive intelligence |

Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds


It never really occurred to me that bird flocks don't have an "alpha" bird leader. In fact, most of the behaviors observed in the animal kingdom are decentralized; that is they are not controlled by a "central controller." This fundamental observation extends well into human systems as well. The economy is a complex interleaved set of human behaviors. When things go wrong, we all too often try to pin the cause on one or two individuals, or groups of individuals. Likewise, many politicians like to take credit for a well performing economy under their watch, but the vast majority of evidence suggests that the overall health of an economy is based on the system itself, and not any one or more individuals, no matter how authoritative their position may be. That is simply the nature of human systems.


Turtles, Termites and Traffic Jams introduces us ways to understand the tendency for systems to become decentralized, and demonstrates tools to observe phenomena through simulation and models. A classic example is the study of a traffic jam. Give simple rules like, "If there is a car close ahead of you, slow down" and "If there aren't any cars close ahead of you, speed up" a traffic system can be simulated. Cars independently slow down and speed up given their own control. However, in an environment where there are constraints, say a police officer with a radar gun, then chain reactions start to emerge. Cars going too fast must react to this condition, thus causing cars behind to slow down when there otherwise would be no apparent reason to do so. This emergent behavior can be described as complex.


Though the author organizes this book into five sections, the book is essentially divided into two parts; the first (Foundations) delivers the underlying principles to decentralized, self-organizing and emergent behavior. The second part covers examples of various phenomena demonstrated via simulation. The result is a thoroughly interesting, though provoking book that leads the reader to want to try the simulations, if not on a computer at least in thought. The subjects of the simulations, often times natural systems such as bugs, frogs, turtles and of course termites, are less important rather than the underlying phenomena that's being explained. Artificial Ants, for instance, demonstrates problem solving skills among a society of near clone-like creatures whose singular goal is success for the colony, rather than individual success. They are able to find food sources, and communicate important information to exploiting those food sources to the fullest. For example, if they find an apple core on the ground, does the whole colony attack it? How many ants should be out looking for more food sources? The answer lies within the mechanics of communication. Using a pheromone, an ant "smells" its way to collect sufficient information to steer it in the direction it would best serve the colony. When a food source is becoming depleted, less pheromone will be apparent causing a greater proportion of ants to go back out and scout for more food sources.


The problems presented, while not in a business context, do have important implications. How can businesses succeed in difficult competitive environments with little information? The demonstrations of systems observed in the natural world give not only inspiration, but important clues to how businesses may survive in the competitive market world. I highly recommend to anyone who is interested in agent-based modeling and the principles it is based upon.


Resnick, M. (1994). Turtles, termites, and traffic jams: Explorations in massively parallel microworlds. Cambridge, Mass: MIT Press.


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What is Agent-based modeling?

Agent-based modeling (ABM) is an approach to studying complex dynamic problems by observing the interactions among autonomous actors (agents) that make up a system. It is broadly recognized that complex phenomenon, such as markets, competition and business in general, have outcomes that are not always easily predicted.

For example, a customer engages in a transaction. That alone is not all that interesting. But faced with a multitude of products all vying for the customer's purchase, demonstrating the outcome of customer decisions in aggregate becomes a bit more tricky. Given the fact customers are not homogeneous, competitive product offerings change and overall external factors have reverberating effects, even the market for a relatively simple good or service becomes a complex phenomena in itself.

Traditional methods to attempt to model markets typically take a "top-down" approach; that is they look at the outcomes in totality and try to break down the components that lead to these results. Conversely, ABM methods are bottoms-up. They simulate behavior by allowing the individual components, or actors, within a model to act according to their own rules based on programmed instructions that simulate these behaviors.

In the customer example above, a traditional approach to modeling a product in this market would be to take sales data for the product, as well as data for the competitive products, and calculate out a market share. Using these data as well as external data sources, a marketing model could then attempt to break down the components of demand by relevant factors such as gender, age, ethnicity, geography, etc....

ABM methods, in contrast, re-create the model environment and the actors involved. A simulation can be created through programming a set of autonomous actors to represent customers and the products they may choose. Customer agents may have instructions that govern their purchase habits, including frequency and quantity of purchase, maximum price willing to pay as well as more qualitative characteristics including aesthetic preferences, values and beliefs. The products themselves can be represented as a different set of agents with their own unique corresponding values of price, quantity (packaging size), aesthetics (color, shape, look, feel, etc...) and values (brand positioning). Even more, these products can have input controls so that an observer may interact with freely each product to adjust settings under a variety of different scenarios. Finally, this simulation can be made dynamic by adding movement of customers in relation to products and measuring within each customer the progress each product makes towards achieving that customer's threshold required for making a purchase. The result is an interactive simulation where variety of scenarios can be tested and potential market outcomes observed with graphic detail.

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