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Abstract
Key Takeaways
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Model Results
Semiconductor Case Study
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References
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Business and Management
Management of Technology and Innovation

Modeling an Innovation Ecosystem with Adaptive Agents

Profile image of Andrew KusiakAndrew Kusiak
https://doi.org/10.1260/1757-2223.3.2.55
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14 pages

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Abstract

Agent-based modeling has proven effective in increasing the understanding of complex systems, including social-economical systems. A goal of modeling complex systems is to distill the system into simple agents with phenotypes guided by simple rules. The model then displays the emergent behavior of these agents interacting with each other and their environment. An agent-based model of innovation and its place in a global economy or ecosystem is presented. The model utilizes simple agents to represent innovating entities such as large corporations and small companies. The results produced by this model reveal the dynamics of innovation and its role in a global economy. The results indicate a large need for partnership in innovation for those entities working within rapidly changing domains. Domains, such as high technology, have constantly changing market expectations, which force innovating entities to seek external sources of assistance to meet these expectations in a timely enough fashion so as to incur benefit. *This paper was edited by Senior Editor Joseph Nadan Section 3. Section 4 the results of this model are presented which illustrate that innovation entities operating within rapidly changing domains must seek innovation partners to cope with the rapidly changing market expectations. The results produced by the model and their contributions to innovation are discussed in Section 5. Finally, conclusions and suggestions for future research are presented in Section 6.

Key takeaways
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  1. The model illustrates the dynamics of innovation within a global ecosystem of 1000 adaptive agents.
  2. Agents in rapidly changing domains require partnerships to meet evolving market expectations effectively.
  3. Emergent behaviors reveal that increased market change rates lead to higher agent interaction and resource accumulation.
  4. The model supports the hypothesis that innovation partnerships enhance success in fast-paced technological markets.
  5. Niche markets exhibit stability, allowing agents to thrive with minimal interaction in slower-changing environments.
Figures (16)
Figure 1. Iterative agent-based model.
Figure 1. Iterative agent-based model.
Figure 1. Mean fitness for an average run of the model with the fitness change rate = 0.01, the cost of interaction = 0.01, and the payoff = 0.01. The initial hypothesis of this model was that the mean interaction rate would increase as the cost of interaction remained low and the payoff for each generation increased. Unfortunately, this hypothesis was proven to be faulty. Fig. 4 — Fig. 8 show that the mean interaction rate remains within a bounded constant region regardless of the cost of interaction and the amount of payoff. The results for runs where the fitness change rate remained constant at 0.05 are presented in Fig. 4 — Fig. 8.
Figure 1. Mean fitness for an average run of the model with the fitness change rate = 0.01, the cost of interaction = 0.01, and the payoff = 0.01. The initial hypothesis of this model was that the mean interaction rate would increase as the cost of interaction remained low and the payoff for each generation increased. Unfortunately, this hypothesis was proven to be faulty. Fig. 4 — Fig. 8 show that the mean interaction rate remains within a bounded constant region regardless of the cost of interaction and the amount of payoff. The results for runs where the fitness change rate remained constant at 0.05 are presented in Fig. 4 — Fig. 8.
Figure 3. Mean agent resource for an average run of the model with the fitness change rate = 0.01, the cost of interaction = 0.01, and the payoff = 0.01.
Figure 3. Mean agent resource for an average run of the model with the fitness change rate = 0.01, the cost of interaction = 0.01, and the payoff = 0.01.
Figure 4. Mean interaction for an average run with cost of interaction = 0.01 and the payoff = 0.01.
Figure 4. Mean interaction for an average run with cost of interaction = 0.01 and the payoff = 0.01.
Figure 5. Mean interaction for an average run with the interaction cost = 0.01 and payoff = 0.05.
Figure 5. Mean interaction for an average run with the interaction cost = 0.01 and payoff = 0.05.
Figure 6. Mean interaction for an average run with the interaction cost = 0.01 and the payoff = 0.0.10. Modeling an Innovation Ecosystem with Adaptive Agents
Figure 6. Mean interaction for an average run with the interaction cost = 0.01 and the payoff = 0.0.10. Modeling an Innovation Ecosystem with Adaptive Agents
Figure 7. Mean interaction for an average run with the interaction cost = 0.01 and payoff = 0.20.
Figure 7. Mean interaction for an average run with the interaction cost = 0.01 and payoff = 0.20.
Figure 8. Mean interaction for an average run with the interaction cost = 0.01 and the payoff = 0.50.
Figure 8. Mean interaction for an average run with the interaction cost = 0.01 and the payoff = 0.50.
Figure 9. Mean resources for an average run with the interaction cost = 0.01 and the payoff = 0.01
Figure 9. Mean resources for an average run with the interaction cost = 0.01 and the payoff = 0.01
Figure 10. Mean resources for an average run with the interaction cost = 0.01 and the payoff = 0.50.
Figure 10. Mean resources for an average run with the interaction cost = 0.01 and the payoff = 0.50.
Figure 11. Mean interaction for an average run with thefitness rate of change = 0.01 Modeling an Innovation Ecosystem with Adaptive Agents
Figure 11. Mean interaction for an average run with thefitness rate of change = 0.01 Modeling an Innovation Ecosystem with Adaptive Agents
igure 13. Mean interaction for an averagerun with the fitnessrate of change = 0.50. Figure 12. Mean resources for an average run with the fitness of rate change = 0.01.
igure 13. Mean interaction for an averagerun with the fitnessrate of change = 0.50. Figure 12. Mean resources for an average run with the fitness of rate change = 0.01.
Figure 14. Mean resources for an averaged run with the fitness rate of change = 0.50.
Figure 14. Mean resources for an averaged run with the fitness rate of change = 0.50.

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