Complex markets are markets whose growth and evolution are primarily driven by personal interactions among potential and current adopters. The personal interactions, typically word of mouth, imitation, and network effects, lie at the micro level of the system, while the aggregate results, typically adoption or sales, lie at the macro level. Agent based modeling including cellular automata and small-world are at the base of the micro-modeling. See also: The Center for Complexity in Business of the Roger H. Smith School of Business, University of Maryland.
Using agent-based modeling tools a "would-be world" can be created to uncover the simple interactions that can lead to what could be considered a complex behavior. The shared pattern of these phenomena is their non-linearity and their surprising outcomes, such as a sudden drop in the adoption rate followed by an unexpected recovery phase.
While analyses in our papers are typically based on R or C++ programs, we found that agent based models can be built using Excel spreadsheets. These spreadsheets can stimulate intuition, and moreover, comprehensive analyses can be conducted using carefully constructed these spreadsheets.
The following are modeling explanations and codes (R and C++) of agent-based models:
in the connectivity data page you'll find two data bases: an authors database that includes author (with his/her ID); Average separation; Cumulative number of papers; Cumulative number of journals s/he published in; First year of publication and last year of publication; and a links database that includes two authors (with their IDs); Cumulative number of joint papers; List of journals at which they published their joint papers.
Examples of agent-based Excel spreadsheet models
Agent based models consist of three components:
(1) A graph representing individuals' behavior. In classical small- world models the world is usually drawn as a circle. In Excel spreadsheets would-be worlds are represented by matrices.
(2) Relationships among neighboring individuals, including strong and weak ties.
(3) Transition rules of the probabilities of adoption between periods.
Our research team:
Research interests: New products ideation; social networks, creativity in marketing; new product development; complexity in marketing
Research interests: customer relationship management (CRM), marketing research and social media.
Research interests: Valuation of customer relationships; customer referral analysis; the evolution of markets for new products, social networks
Research interests: Word of mouth communications, rumors and urban legends, complexity in marketing, resistance to innovation
Eitan Muller - Interdisciplinary Center (IDC) Herzliya and New York University
Research interests: Hi-Tech marketing, innovation diffusion and the evolution of markets for new products, social networks
Research interests: The evolution of markets for new products; industrial marketing, social networks
Research interests: Complexity of market dynamics, diffusion of innovation,
emergence of collective behavior in social systems, social networks
Agent-based market with externalities
This Excel-based spreadsheet exemplifies the basic framework of agent-based model
This Excel-based cellular automata program simulates a market with two sub-markets: early market of technophile adopters, and a main market of functionality-seeking
This Excel-based program simulates a market with network externalities - the utility of an adopter increases with the number of other adopters. What the model shows is the chilling effects of network externalities - that is - the diffusion and adoption of network products is slower.
This Excel-based program exemplifies a market in which each individual communicates with all of his or her immediate neighbors. In addition, the entire market is divided into "communities", where the strength of the word-of-mouth activities varies depending on whether the communications are within a community or between communities.