Carlos Gershenson
I am a Mexican researcher. I have a wide variety of academic interests, including self-organizing systems, complexity, artificial life, evolution, cognition, artificial societies, and philosophy. I am a full time researcher at the Computer Sciences Department of the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) of the Universidad Nacional Autónoma de México (UNAM). I am also a researcher at the Centro de Ciencias de la Complejidad (C3) of the UNAM. I am Editor-in-Chief of Complexity Digest, Book Review Editor of Artificial Life, Complexity-at-Large Editor of Complexity, and webmaster of alife.org.
Phone: +52 55 56 22 36 19
Address: Dr. Carlos Gershenson
Computer Sciences Department
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas
Universidad Nacional Autónoma de México
Ciudad Universitaria, A.P. 20-726
01000 México D.F., México
Phone: +52 55 56 22 36 19
Address: Dr. Carlos Gershenson
Computer Sciences Department
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas
Universidad Nacional Autónoma de México
Ciudad Universitaria, A.P. 20-726
01000 México D.F., México
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We develop a comprehensive methodology for describing the topology of the perceived interactions among stakeholders and characterizing them with the content of their discourses about moorland conservation. On one side, with this methodology we obtained:
1.The networks or “ego-centered networks” based on the stakeholders or actors interviews.
2. A complete network by merging ego-centered networks.
3. Synergistic/antagonistic perceptions towards actors were represented in weighted networks.
4. The identification of leaders among the interviewees.
On the other, interviews were analyzed with text mining, providing social and semantic context about the moorland conservation discourses. We finally integrated both approaches into a single framework.
Results illustrate the social structure of the moorland's leaders, their cultural context and their perceptions about the importance of the moorland ecosystem. On this basis, decision and policy-making for institutional and stakeholder actors could be improved. Moreover, the studied social system could be described in terms for guided self-organization.
We develop a comprehensive methodology for describing the topology of the perceived interactions among stakeholders and characterizing them with the content of their discourses about moorland conservation. On one side, with this methodology we obtained:
1.The networks or “ego-centered networks” based on the stakeholders or actors interviews.
2. A complete network by merging ego-centered networks.
3. Synergistic/antagonistic perceptions towards actors were represented in weighted networks.
4. The identification of leaders among the interviewees.
On the other, interviews were analyzed with text mining, providing social and semantic context about the moorland conservation discourses. We finally integrated both approaches into a single framework.
Results illustrate the social structure of the moorland's leaders, their cultural context and their perceptions about the importance of the moorland ecosystem. On this basis, decision and policy-making for institutional and stakeholder actors could be improved. Moreover, the studied social system could be described in terms for guided self-organization.