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Risk-return relationship in a complex adaptive system.

ABSTRACT: For survival and development, autonomous agents in complex adaptive systems involving the human society must compete against or collaborate with others for sharing limited resources or wealth, by using different methods. One method is to invest, in order to obtain payoffs with risk. It is a common belief that investments with a positive risk-return relationship (namely, high risk high return and vice versa) are dominant over those with a negative risk-return relationship (i.e., high risk low return and vice versa) in the human society; the belief has a notable impact on daily investing activities of investors. Here we investigate the risk-return relationship in a model complex adaptive system, in order to study the effect of both market efficiency and closeness that exist in the human society and play an important role in helping to establish traditional finance/economics theories. We conduct a series of computer-aided human experiments, and also perform agent-based simulations and theoretical analysis to confirm the experimental observations and reveal the underlying mechanism. We report that investments with a negative risk-return relationship have dominance over those with a positive risk-return relationship instead in such a complex adaptive systems. We formulate the dynamical process for the system's evolution, which helps to discover the different role of identical and heterogeneous preferences. This work might be valuable not only to complexity science, but also to finance and economics, to management and social science, and to physics.

SUBMITTER: Song K

PROVIDER: S-EPMC3316582 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Risk-return relationship in a complex adaptive system.

Song Kunyu K An Kenan K Yang Guang G Huang Jiping J

PloS one 20120330 3

For survival and development, autonomous agents in complex adaptive systems involving the human society must compete against or collaborate with others for sharing limited resources or wealth, by using different methods. One method is to invest, in order to obtain payoffs with risk. It is a common belief that investments with a positive risk-return relationship (namely, high risk high return and vice versa) are dominant over those with a negative risk-return relationship (i.e., high risk low ret ...[more]

PMID: 22479416

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Project description:BackgroundThe concept of complexity is used in palliative care (PC) to describe the nature of patients' situations and the extent of resulting needs and care demands. However, the term or concept is not clearly defined and operationalised with respect to its particular application in PC. As a complex problem, a care situation in PC is characterized by reciprocal, nonlinear relations and uncertainties. Dealing with complex problems necessitates problem-solving methods tailored to specific situations. The theory of complex adaptive systems (CAS) provides a framework for locating problems and solutions. This study aims to describe criteria contributing to complexity of PC situations from the professionals' view and to develop a conceptual framework to improve understanding of the concept of "complexity" and related elements of a PC situation by locating the complex problem "PC situation" in a CAS.MethodsQualitative interview study with 42 semi-structured expert (clinical/economical/political) interviews. Data was analysed using the framework method. The thematic framework was developed inductively. Categories were reviewed, subsumed and connected considering CAS theory.ResultsThe CAS of a PC situation consists of three subsystems: patient, social system, and team. Agents in the "system patient" are allocated to further subsystems on patient level: physical, psycho-spiritual, and socio-cultural. The "social system" and the "system team" are composed of social agents, who affect the CAS as carriers of characteristics, roles, and relationships. Environmental factors interact with the care situation from outside the system. Agents within subsystems and subsystems themselves interact on all hierarchical system levels and shape the system behaviour of a PC situation.ConclusionsThis paper provides a conceptual framework and comprehensive understanding of complexity in PC. The systemic view can help to understand and shape situations and dynamics of individual care situations; on higher hierarchical level, it can support an understanding and framework for the development of care structures and concepts. The framework provides a foundation for the development of a model to differentiate PC situations by complexity of patients and care needs. To enable an operationalisation and classification of complexity, relevant outcome measures mirroring the identified system elements should be identified and implemented in clinical practice.

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Risk-return relationship in a complex adaptive system.

Publications

Risk-return relationship in a complex adaptive system.

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