Twelve Distinctive Techniques In order to Avoid ARAF Troubles

This is reminiscent of the famous Prisoners' Dilemma, which is a symmetric game (Stark, 2010). In fact, the cause for instability is similar in both games: it is the temptation (incentive) to leave the mutually beneficial state. The difference between the two games is that, in the Prisoners' Dilemma, both players are tempted in this way while in the killer-diplomat game, there is a temptation for the parasite only. 3.2. Agent-based modeling Agent-based models (ABMs) have become a powerful tool for tackling complex systems, where the individuality, temporal state and spatial distribution of its players may be of importance. They are typically characterized by numerous interacting entities, often called agents or individuals (depending on the discipline so that the term individual-based model (IBM) is used as well). They pursue certain objectives (e.g., increasing fitness, yield, status) by following, more or less, simple structured rules. These agents can be mobile or stationary units within a continuous or discrete environment defined by three, two, one or even no spatial dimension. In silico environments without any dimension simply imply that the modeled system behavior is presumably independent of any spatial scale. Including more dimensions assumes that this may be of importance for the behavior of the system: A model investigating the hunting strategies of a terrestrial predator may be sufficiently described by a two-dimensional environment. Whereas a third dimension has to be considered simulating the movement of immune-cells through different tissues or in the blood. The philosophy of ABMs is to slice problems on the macro-level down to simple interaction- and reaction-rules of players on a micro-level. For ARAF example, patterns occurring on the population level are transferred to properties and the behavior of single individuals. Diseases of an individual can be explained by the malfunction or disorder of organs and tissues. Often the macro-level behavior of a system cannot be foreseen by only summing up the rules of players. Instead, patterns may arise from the complex interdigitation of state-dependent behavior of its entities, an effect called emergence. Resolving a macro-level pattern (emergence of a certain behavior) to a lower complexity level comes at the price of a detailed knowledge of the individuals properties and behavioral strategies, which have to be precisely formulated. Especially models representing a biological system frequently deal with several involved types of agents (e.g., food-webs, stability of ecosystems) and numerous interactions often require a bottom-up modeling approach with a deep knowledge of individual properties. Thus, ABMs are typically hungry for data (e.g., thresholds for reaction to signals, kinetic parameters) and computationally expensive due to, e.g.