Complexity: A Review of the Classics

Furtado, Bernardo Alves; Sakowski, Patrícia Alessandra Morita

doi:10.18278/jpcs.1.2.1

Cited by 7 publications

(15 citation statements)

References 29 publications

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“…The seventh element comes from the observation that most social systems exhibit hallmarks of complex systems. Different authors have proposed different definitions for those hallmarks (Arthur, 2013; Forrester, 1971; Furtado and Sakowski, 2014; Mitchell, 2011; Richardson, 1996; Sterman, 2000). We refrain from delving into an extensive discussion, preferring to identify the most relevant complexity markers based on the major points of convergence found in the literature: feedback loops, endogeneity, delays, policy resistance, path dependence, points of exploitation (i.e.…”

Section: Understanding Complex Social Problems: the Complex Social Problem Frameworkmentioning

confidence: 99%

A tale of complexity

Carvalho

Mazzon

Santos

2018

JSOCM

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Purpose The purpose of this paper is the development of a framework to address complex social problems. The paper proposes an integrative framework inspired in complexity sciences, using it to explain the demise of cigarettes in recent decades. Design/methodology/approach The paper uses the method of system dynamics to represent the complexity inherent in most social ecosystems where social marketers operate. Findings The framework identifies the major determinants of complex problems in social ecosystems, giving emphasis to the role performed by endogenous social structures. The paper presents the results of a simulation replicating the evolution of perceived attractiveness of cigarettes in recent decades, highlighting the role of the Surgeon General’s 1964 report in the USA as a catalyst force that accelerated the process of change. Research limitations/implications The limitations derive from the use of the system dynamics method, in particular the high level of aggregation of variables. Implications include the potential for increased cross-fertilization between social marketing and other disciplines concerned with social change. Practical implications The eight elements that compose the proposed framework can be identified or applied to any social ecosystem, helping in the identification of points with high leverage for social change. Social implications Proper understanding of how complex social problems arise is vital to increase the odds of success of social marketing interventions. The paper also highlights common threads in the development of problems in different social ecosystems. Originality/value The paper presents a novel framework for addressing the complexity inherent to the social ecosystems where social marketers operate.

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Section: Understanding Complex Social Problems: the Complex Social Problem Frameworkmentioning

confidence: 99%

A tale of complexity

Carvalho

Mazzon

Santos

2018

JSOCM

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“…In particular, there are certain mechanisms that are very difficult to represent in an EBM. Examples include learning (Furtado and Sakowski 2014), true uncertainty (Pyka and Fagiolo 2007), or the endogenous formation of preferences (Hodgson and Knudsen 2004). 6 This remains true even if, as noted above, important progress in conventional economic modeling has been made in incorporating some of these aspects into EBMs.…”

Section: Epistemological Considerationsmentioning

confidence: 99%

Getting the Best of Both Worlds? Developing Complementary Equation-Based and Agent-Based Models

et al. 2017

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We argue that building agent-based and equation-based versions of the same theoretical model is a fruitful way of gaining insights into real-world phenomena. We use the epistemological concept of "models as isolations and surrogate systems" as the philosophical underpinning of this argument. In particular, we show that agent-based and equation-based approaches align well when used simultaneously and, contrary to some common misconceptions, should be considered complements rather than substitutes. We illustrate the usefulness of the approach by examining a model of the long-run relationship between economic development and inequality (i.e., the Kuznets hypothesis).

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“…In Wolfram’s case the spatial-temporal pattern is determined by the interaction rule. Following his classification, four kind of behaviors can emerge in the one-dimensional automata, namely homogeneous, periodic, chaotic and complex [ 18 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Complex Systems Built as Coupled Physical, Communication and Decision Layers

Kühnlenz

Nardelli

2016

PLoS ONE

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This paper proposes a simple model to capture the complexity of multilayer systems where their constituent layers affect, and are affected by, each other. The physical layer is a circuit composed by a power source and resistors in parallel. Every individual agent aims at maximizing its own delivered power by adding, removing or keeping the resistors it has; the delivered power is in turn a non-linear function that depends on the other agents’ behavior, its own internal state, its global state perception, the information received from its neighbors via the communication network and a randomized selfishness. We develop an agent-based simulation to analyze the effects of number of agents (system size), communication network topology, communication errors and the minimum power gain that triggers a behavioral change on the system dynamic. Our results show that a wave-like behavior at macro-level (caused by individual changes in the decision layer) can only emerge for a specific system size. The ratio between cooperators and defectors depends on the minimum gain assumed—lower minimal gains lead to less cooperation, and vice-versa. Different communication network topologies imply different levels of power utilization and fairness at the physical layer, and a certain level of error in the communication layer induces more cooperation.

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Complexity: A Review of the Classics

Cited by 7 publications

References 29 publications

A tale of complexity

A tale of complexity

Getting the Best of Both Worlds? Developing Complementary Equation-Based and Agent-Based Models

Dynamics of Complex Systems Built as Coupled Physical, Communication and Decision Layers

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