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Risk-return relationship in a complex adaptive system.

Song K, An K, Yang G, Huang J - PLoS ONE (2012)

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Fudan University, Shanghai, China.

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.

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Same as Figure 4, but showing the distribution of preferences.
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pone-0033588-g005: Same as Figure 4, but showing the distribution of preferences.

Mentions: , and the return, , for (a)–(f) 24 subjects and (g)–(l) 1000 agents at various 's. (a)–(f) Data of the human experiments (total 30 rounds for each ); (g)–(l) Data of the agent-based computer simulations (total 800 rounds for each , with additional 200 rounds performed at the beginning of the simulations; during the 200 rounds, we train all of the strategies by scoring them whereas the wealth of each agent remains unchanged). Here is Agent 's wealth at the end of rounds (the total number of rounds, , is for the experiments and simulations, respectively), and is Agent 's initial wealth. All of the subjects or agents are divided into two groups with preference (red squares) and preference = 1 (blue dots). Here, the “preference” is given by , where is the number of times for subjects or agents to choose Room 1 within the total rounds. The values or distribution of the preferences of the subjects or agents can be found in Figs. 4 and 5. Here, “Linear Fit” denotes the line fitting the data in each panel using the least square method, which serves as a guide for the eye. (The fitting functions are listed in Table 2.) All of the lines are downward, which indicate a statistically negative relationship between risk and return. The present negative relationship just reflects the dominance of investments with a negative RRR in the whole system, in spite of a relatively small number of investments with a positive RRR. Other parameters: (g)–(l) and .


Risk-return relationship in a complex adaptive system.

Song K, An K, Yang G, Huang J - PLoS ONE (2012)

Same as Figure 4, but showing the distribution of preferences.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3316582&req=5

pone-0033588-g005: Same as Figure 4, but showing the distribution of preferences.
Mentions: , and the return, , for (a)–(f) 24 subjects and (g)–(l) 1000 agents at various 's. (a)–(f) Data of the human experiments (total 30 rounds for each ); (g)–(l) Data of the agent-based computer simulations (total 800 rounds for each , with additional 200 rounds performed at the beginning of the simulations; during the 200 rounds, we train all of the strategies by scoring them whereas the wealth of each agent remains unchanged). Here is Agent 's wealth at the end of rounds (the total number of rounds, , is for the experiments and simulations, respectively), and is Agent 's initial wealth. All of the subjects or agents are divided into two groups with preference (red squares) and preference = 1 (blue dots). Here, the “preference” is given by , where is the number of times for subjects or agents to choose Room 1 within the total rounds. The values or distribution of the preferences of the subjects or agents can be found in Figs. 4 and 5. Here, “Linear Fit” denotes the line fitting the data in each panel using the least square method, which serves as a guide for the eye. (The fitting functions are listed in Table 2.) All of the lines are downward, which indicate a statistically negative relationship between risk and return. The present negative relationship just reflects the dominance of investments with a negative RRR in the whole system, in spite of a relatively small number of investments with a positive RRR. Other parameters: (g)–(l) and .

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Fudan University, Shanghai, China.

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.

Show MeSH