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

Behaviorally linked location hierarchies

Though the concept of hierarchy has been used extensively in the siting of multilevel service systems, particular attention has not been paid by analysts to the full variety of ways in which locational goals of different service levels can be linked. Many past studies of locational hierarchies, for example, have been merely structural elaborations of common single-level location problems, with little thought given to the way in which service goals can or should be linked. The purpose of this analysis is to develop multilevel siting models which incorporate spatial expressions of the functional interdependence between service levels. More importantly, however, this paper suggests the necessity for differentiating between two broad categories of hierarchical location systems, those which are technologically linked and those which are behaviorally linked . The basis of this differentiation is the type of spatial information which is communicated from one organization level to another. Furthermore, these two types of hierarchical systems are shown to have locational goal structures which can be exploited effectively in various planning scenarios.

A multi-scale computational approach to understanding cancer metabolism

A first principles Nash equilibrium approach to modeling, simulation, and analysis of metabolic pathways is presented. The modeling framework is described in detail, and small examples illustrating mass and charge balancing, the inclusion of enzymatic reactions in the model, constraint linear independence, and allosteric inhibition are given in order to provide a tutorial for the reader. The methodology is then applied to the methionine salvage pathway in order to demonstrate that it can correctly capture the behavior of an important pathway in the study of cancer. It is shown that methylthioadenosine (MTA) accumulation as a result of the loss of activity of the enzyme S-methyl-5′-thioadenosine phosphorylase (MTAP) is correctly predicted by the Nash equilibrium approach under tight regulation of adenine. Several examples are presented to elucidate the key ideas in modeling cancer metabolism using the Nash equilibrium approach