Risk-Return Relationship in a Complex Adaptive System

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

Agricultural Carbon Emissions Embodied in China’s Foreign Trade and Its Driving Factors

Since the development of global trade, the involvement of agriculture in globalization has been increasing. Globalization and trade have led to the separation of production and consumption, triggering a worldwide relocation of agricultural carbon emissions (ACE). By linking a global ACE database to a global multi-regional input-output (MRIO) model, this paper calculates the ACE embodied in China’s foreign trade. Moreover, by using the Logarithmic Mean Divisia Index (LMDI) decomposition method, it analyzes the impacts of embodied ACE intensity, trade scale, industrial structure, economic development and consumption levels, and population on China’s ACE. We found that the impact of globalization on China’s ACE is gradually increasing. China has shifted from a net ACE exporter (the net export volume in 1961 was 13.52 million tons) to a net ACE importer (the net import volume in 2016 was 40.35 million tons). By investigating the underlying mechanisms, we found that the dominant factor was the inhibitory effect of the decline in the embodied ACE intensity of China, contributing 73% to the increase in net import volume, followed by the expansion of trade and the decline in the proportion of agricultural output value in GDP, with contribution rates of 17 and 10%, respectively

Hippocampal Neurogenesis and the Brain Repair Response to Brief Stereotaxic Insertion of a Microneedle

We tested the hypothesis that transient microinjury to the brain elicits cellular and humoral responses that stimulate hippocampal neurogenesis. Brief stereotaxic insertion and removal of a microneedle into the right hippocampus resulted in (a) significantly increased expression of granulocyte-colony stimulating factor (G-CSF), the chemokine MIP-1a, and the proinflammatory cytokine IL12p40; (b) pronounced activation of microglia and astrocytes; and (c) increase in hippocampal neurogenesis. This study describes immediate and early humoral and cellular mechanisms of the brain’s response to microinjury that will be useful for the investigation of potential neuroprotective and deleterious effects of deep brain stimulation in various neuropsychiatric disorders

Hippocampal Neurogenesis and the Brain Repair Response to Brief Stereotaxic Insertion of a Microneedle

We tested the hypothesis that transient microinjury to the brain elicits cellular and humoral responses that stimulate hippocampal neurogenesis. Brief stereotaxic insertion and removal of a microneedle into the right hippocampus resulted in (a) significantly increased expression of granulocyte-colony stimulating factor (G-CSF), the chemokine MIP-1a, and the proinflammatory cytokine IL12p40; (b) pronounced activation of microglia and astrocytes; and (c) increase in hippocampal neurogenesis. This study describes immediate and early humoral and cellular mechanisms of the brain’s response to microinjury that will be useful for the investigation of potential neuroprotective and deleterious effects of deep brain stimulation in various neuropsychiatric disorders

Research on Line Patrol Strategy of 110kV Transmission Line after Lightning Strike

Lightning faults occupy in the majority of instantaneous fault and reclosing can usually be successful, so power supply can be restored without immediate patrol in many cases. Firstly, this paper introduces the lightning fault positioning and identifying method. Then test electrical performance of insulators after lightning strike from 110kV lines. Data shows that lightning strike has little effect on the electric performance of insulator. Finally, illustrating disposal process of the 110 kV transmission line after lightning fault, certifying that the power supply reliability be ensured without line patrol

Experimental data of 's for six 's within 30 rounds.

<p>Experimental data of 's for six 's within 30 rounds.</p

Research on Line Patrol Strategy of 110kV Transmission Line after Lightning Strike

Lightning faults occupy in the majority of instantaneous fault and reclosing can usually be successful, so power supply can be restored without immediate patrol in many cases. Firstly, this paper introduces the lightning fault positioning and identifying method. Then test electrical performance of insulators after lightning strike from 110kV lines. Data shows that lightning strike has little effect on the electric performance of insulator. Finally, illustrating disposal process of the 110 kV transmission line after lightning fault, certifying that the power supply reliability be ensured without line patrol

Averaged ratio,

<p><b>, versus </b><b> for the human experiments with 24 subjects (red squares) and agent-based computer simulations with 1000 agents (blue dots).</b> Here “” denotes the average over the total 30 experimental rounds (experimental data of for each round are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033588#pone-0033588-t001" target="_blank">Table 1</a>) or over the 800 simulation rounds (the additional 200 rounds were performed at the beginning of the simulation for each M1/M2; during the 200 rounds, we train all of the strategies by scoring them whereas the wealth of each agent remains unchanged). All the experimental and simulation points lie in or beside the diagonal line (“slope = 1”), which is indicative of . Parameters for the simulations: and .</p

Same as <b>Figure 2(g)–(l)</b>, but showing the relationship between the risk,

<p><b>, and the relative wealth, </b><b>, on a logarithmic scale.</b> “Linear Fit” corresponds to the line fitting the data of preference or preference using the least square method, which serves as a guide for the eye. (The fitting functions are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033588#pone-0033588-t003" target="_blank">Table 3</a>.)</p

Preferences of (a)–(f) the 24 subjects in the human experiments (plotted in the bar graph) or (g)–(l) the 1000 agents in the agent-based computer simulations, for various

<p><b>'s.</b> Here, “Mean” denotes the preference value averaged for (a)–(f) the 24 subjects or (g)–(l) 1000 agents. In (a)–(f), the present 24 subjects are ranked by their risk (namely, their investing weight) from low to high, within the range (a) [0.16, 1], (b) [0.01, 1], (c) [0.02, 1], (d) [0.16, 1], (e) [0.31, 1], and (f) [0.29, 1]; see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033588#pone-0033588-t004" target="_blank">Table 4</a> for details. Similarly, in (g)–(l), the 1000 agents are ranked by their risk from low to high, within the range (0, 1] assigned according to the code “(double)rand()” in the C programming language. In (a)–(f), the ratio between the numbers of subjects with “preference = 1” and “preference” are, respectively, (a) 2/22, (b) 4/20, (c) 5/19, (d) 7/17, (e) 11/13, and (f) 8/16. In (g)–(l), the ratio between the numbers of agents with “preference = 1” and “preference” are, respectively, (g) 2/998, (h) 23/977, (i) 94/906, (j) 233/767, (k) 200/800, and (l) 220/780.</p