For anyone interested in the spread of information among a human population, it is important to understand whether there are individuals who exert more impact on the knowledge, perceptions, and behavior than others. Social scientists interested in the causes of social change can find root causes in the existence, number, and behavior of influentials. The rapidly growing number of researchers interested in social networks can add another explanatory variable to the array of “physical” constraints of network structures. For marketers, politicians, and activists interested in actionable strategies, targeting influentials could increase the efficiency of their campaigns.
“The Effects of Word-of-Mouth: An Agent-Based Simulation of Interpersonal Influence in Social Networks”, Word of Mouth Research Symposium, 2007.
“How Word of Mouth Impacts Medicare Product Launch and Product Design”, Measuring Word of Mouth Vol. 3, Word of Mouth Research Symposium, 2007.
In 2005 we began working with Humana’s Senior Products group to help them plan their marketing strategy for new product launches that were planned for 2006, when Medicare plans were mandated by law to begin including prescription drug coverage. Our tool helped Humana make a number of important predictions and it elucidated key aspects of how WOM would impact the outcome of their marketing initiatives.
One year after the launch of the new Medicare plans, Humana had moved from sixth largest to second largest provider of Medicare plans in the United States.
“Swarm Intelligence: A Whole New Way to Think About Business”, Harvard Business Review, 2001.
Order online through the Harvard Business Review website or request a free reprint from Icosystem. A little more than a year ago, Southwest Airlines was having trouble with its cargo operations. Even though the average plane was using only 7% of its cargo space, at some airports there wasn’t enough capacity to accommodate scheduled loads of freight, leading to bottlenecks throughout Southwest’s cargo routing and handling system. At the time, employees were trying to load freight onto the first plane going in the right direction–a seemingly reasonable strategy. But because of it, workers were spending an unnecessary amount of time moving cargo around and sometimes filling aircraft needlessly. To solve its problem, Southwest turned to an unlikely source: ants. Specifically, researchers looked at the way ants forage, using simple rules, always finding efficient routes to food sources. When they applied this research to Southwest’s problem, they discovered something surprising: it can be better to leave cargo on a plane headed initially in the wrong direction. If, for example, they wanted to send a package from Chicago to Boston, it might actually be more efficient to leave it on a plane heading for Atlanta and then Boston than to take it off and put it on the next flight to Boston. Applying this insight has slashed freight transfer rates by as much as 80% at the busiest cargo stations, decreased the workload for the people who move cargo by as much as 20%, and dramatically reduced the number of overnight transfers. That’s allowed Southwest to cut back on its cargo storage facilities and minimize wage costs. In addition, fewer planes are now flying full, which opens up significant opportunities for the company to generate new business. Thanks to the improvements, Southwest estimates an annual gain of more than $10 million.
“Swarm Intelligence: From Natural to Artificial Systems”, Oxford University Press, 1999.
Swarm intelligence offers another way of designing “intelligent” systems, where autonomy, emergence, and distributed functioning replace control, preprogramming, and centralization. This book surveys several examples of swarm intelligence in social insects and describes how to design distributed algorithms, multi-agent systems, and groups of robots according to the social insect metaphor.
“Dynamic Scheduling and Division of Labor in Social Insects”, Adaptive Behavior, 2001.
Download (PDF) Market-based algorithms have been introduced several years ago as a new paradigm for controlling complex, unpredictable systems. In a market-based algorithm, resources or tasks are allocated efficiently through a market-clearing mechanism. Agents bid for resources and the agent with the highest bid gets the resource. The market-clearing mechanism ensures that all tasks or resources have been allocated. Agents adjust their bids according to prior successes or failures in getting the resource. Social insects -ants, bees, termites and wasps-provide us with another metaphor for controlling complex systems. A social insect colony is a complex system often characterized by division of labor: workers tend to be specialized in certain tasks. But this specialization is flexible. The flexibility of task allocation exhibited at the colony level is connected to the elasticity of individual workers.
“Agent-Based Modeling: Methods and Techniques for Simulating Human Systems”, Proc. National Academy of Science, 2002.
Download (PDF) After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed.
“Scale-Free Networks”, Scientific American, May 2003.
Download (PDF) The brain is a network of nerve cells connected by axons, and cells themselves are networks of molecules connected by biochemical reactions. Societies, too, are networks of people linked by friendships, familial relationships and professional ties. On a larger scale, food webs and ecosystems can be represented as networks of species. And networks pervade technology: the Internet, power grids and transportation systems are but a few examples. Even the language we are using to convey
these thoughts to you is a net- work, made up of words connected by syntactic relationships. Yet despite the importance and pervasiveness of networks, scientists have had little understanding of their structure and properties. How do the interactions of several malfunctioning nodes in a complex genetic network result in cancer? How does diffusion occur so rapidly in certain social and communications systems, leading to epidemics of diseases and computer viruses? How do some net- works continue to function even after the vast majority of their nodes have failed?
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Check out our presentation from 2006.
Computer Simulation Developers
Icosystem Corporation is a successful, growing company with world-class expertise in complexity science and evolutionary computing. Our work combines cutting-edge scientific research and applied client projects that range from social network analysis and consumer behavior modeling to interactive evolutionary search and design. Icosystem provides an intellectually stimulating work environment and a unique opportunity to bridge the scientific and commercial worlds. Our Cambridge, MA location fosters interactions with a vibrant academic and technological community.
We are always looking for highly talented Java developers to help us engineer industrial strength computer simulations for our leading-edge research and consulting projects. You will work on client engagements and internal research assignments in small teams with our scientists and experienced modelers in a creative and highly collaborative environment.