scholarly journals Integrated sustainability policy assessment – an agent-based ecological-economic model

Author(s):  
Sylvie Geisendorf ◽  
Christian Klippert

AbstractThe paper proposes an agent-based evolutionary ecological-economic model that captures the link between the economy and the ecosystem in a more inclusive way than standard economic optimization models do. We argue that an evolutionary approach is required to understand the integrated dynamics of both systems, i.e. micro–macro feedbacks. In the paper, we illustrate that claim by analyzing the non-triviality of finding a sustainability policy mix as a use case for such a coupled system. The model has three characteristics distinguishing it from traditional environmental and resource economic models: (1) it implements a multi-dimensional link between the economic and the ecological system, considering side effects of production, and thus combines the analyses of environmental and resource economics; (2) following literature from biology, it uses a discrete time approach for the biological resource allowing for the whole range of stability regimes instead of artificially stabilizing the system, and (3) it links this resource system to an evolving, agent-based economy (on the basis of a Nelson-Winter model) with bounded rational decision makers instead of the standard optimization model. The policy case illustrates the relevance of the proposed integrated assessment as it delivers some surprising results on the effects of combined and consecutively introduced policies that would go unnoticed in standard models.

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-26
Author(s):  
Friederike Wall

Coordination among decision-makers of an organization, each responsible for a certain partition of an overall decision-problem, is of crucial relevance with respect to the overall performance obtained. Among the challenges of coordination in distributed decision-making systems (DDMS) is to understand how environmental conditions like, for example, the complexity of the decision-problem to be solved, the problem’s predictability and its dynamics shape the adaptation of coordination mechanisms. These challenges apply to DDMS resided by human decision-makers like firms as well as to systems of artificial agents as studied in the domain of multiagent systems (MAS). It is well known that coordination for increasing decision-problems and, accordingly, growing organizations is in a particular tension between shaping the search for new solutions and setting appropriate constraints to deal with increasing size and intraorganizational complexity. Against this background, the paper studies the adaptation of coordination in the course of growing decision-making organizations. For this, an agent-based simulation model based on the framework of NK fitness landscapes is employed. The study controls for different levels of complexity of the overall decision-problem, different strategies of search for new solutions, and different levels of cost of effort to implement new solutions. The results suggest that, with respect to the emerging coordination mode, complexity subtly interferes with the search strategy employed and cost of effort. In particular, results support the conjecture that increasing complexity leads to more hierarchical coordination. However, the search strategy shapes the predominance of hierarchy in favor of granting more autonomy to decentralized decision-makers. Moreover, the study reveals that the cost of effort for implementing new solutions in conjunction with the search strategy may remarkably affect the emerging form of coordination. This could explain differences in prevailing coordination modes across different branches or technologies or could explain the emergence of contextually inferior modes of coordination.


2013 ◽  
Vol 21 (3) ◽  
pp. 141-143 ◽  
Author(s):  
Harn Wei Kua ◽  
Asanga Gunawansa

Author(s):  
Einar Jón Erlingsson ◽  
Marco Raberto ◽  
Hlynur Stefánsson ◽  
Jón Thór Sturluson

Author(s):  
Niaona Zhang ◽  
Xiaofang Zhang ◽  
Xuanxuan Bao ◽  
Xiwen Oin

2013 ◽  
Vol 74 (4) ◽  
pp. 697-709 ◽  
Author(s):  
V. I. Gurman ◽  
G. A. Matveev ◽  
E. A. Trushkova

Author(s):  
Chen Lian

Abstract Unlike in standard models, decision makers often “narrowly bracket” and make each decision in isolation. I develop a new approach, which I term narrow thinking, to systematically model narrow bracketing. The definition of narrow thinking is that different decisions are based on different, non-nested, information. As a result, the narrow thinker makes each decision with imperfect knowledge of other decisions and faces difficulties coordinating her multiple decisions. The narrow thinker effectively cares less about her other decisions when making each decision. The main application of narrow thinking is to provide a “smooth” model of mental accounting without requiring the decision maker to have explicit budgets. My approach generates unique predictions about how the degree of mental accounting depends on expenditure shares and cognitive limitations. It also illustrates how narrow bracketing and mental accounting can be explained by the same underlying friction.


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