 As we talked about in a previous module, nonlinear causality is a form of causation where cause and effect can flow in a bidirectional fashion between two or more elements and where a single effect may have multiple causes or vice versa. The essential characteristic of nonlinear causality is the idea of feedback that an effect can create a cause but equally this cause can then feedback to create an effect in the first system. Nonlinear causality can be contrasted with linear causality where the direction of an effect flows in a unidirection. Nonlinear causality leads to a number of important outcomes that are not possible when considering more simple circumstances involving linear causality. Nonlinear causality can lead to self-reinforcing or self-amplifying processes through feedback loops thus allowing for disproportionality between initial cause and final effect. The second important outcome to nonlinear causality is the bidirectional flow of causation between the macro and micro levels within a system thus enabling downward causation as well as upward causation. Thirdly it can allow for reverse causation in time that is to say set future goals can feedback to affect current events. Likewise nonlinear causality implies the action of many variables in creating a cause or vice versa which can lead to equifinality the idea that some end effect may be created or reached through a number of different pathways. Finally in contrast to linear causality that creates a deterministic vision of the world nonlinear causality may lead to the indetermination of outcomes. Feedback processes are a central characteristic of nonlinear causation. Events do not happen in isolation but instead feedback to affect their source. Thus nonlinear causality can have the characteristic of being self-perpetuating or self-reinforcing. For example when we look at some relationship dynamic between a parent and child we might note that the child is uninterested in the parents because her parents showed no interest in her but equally her parents showed no interest in her because she appeared uninterested in the first place. This is a self-reinforcing loop enabled by nonlinear causality. Likewise the formation of hurricanes, financial crisis, animal stampede or the development of new cultures would be other examples of processes driven by self-reinforcing cause and effect. Whereas linear causation is defined by a degree of proportionality between a given cause and its effect. Nonlinear causality driven by this feedback can enable a disproportionality between cause and effect what is called the butterfly effect. Because of feedback loops small events can get compounded through each causal feedback iteration enabling a rapid change in proportionality between the inputs and outputs to the system. The results of this can be widely divergent outcomes to some situation depending on small changes in input values what is called sensitivity to initial conditions. Whereas linear causation is predicated on being able to isolate a single or small amount of variables causing a given effect. In searching for nonlinear causation we do not try to reduce the number of reasons for a given effect or vice versa the number of effects deriving from a cause. For example if we took the linear reductionist paradigm and asked the question why does an airplane fly this approach would try to reduce the cause to a limited number of direct physical interactions in which case the answer would be traced back to the dynamics of the airflow around the wing as described by a few variables within the Navier-Stokes equations of fluid dynamics. Inversely a nonlinear causal description may involve many different explanations to this question. We might say the flight is caused by the pilot directing the plane to its destination or the flight is caused by the fact that it was chartered to fly at that particular time or that it is flying because the company can make a profit from putting on that flight. Without any of these factors the flight would not be happening. When we take this non-reductive more holistic interpretation to causality it's possible to see that some effects are the product of an almost infinite number of interacting factors and it stops making sense to ask about a single direct effect. Instead the language switches to that of emergence asking how many different factors interact in a specific fashion to create a given outcome. With emergent phenomena creating higher level patterns that then feed back to exert an effect on the lower level more elementary parts. Whereas linear causality implies that causation flows from the bottom up but not in both directions. Nonlinear causality and the idea of emergence enable the interpretation of events as both upwardly and downwardly caused with causation flowing bi-directionally from the micro to the macro and back again. To illustrate this we might think about a polar bear and ask why is the polar bear colored white? The reason that it is white one might say is because of its genotype which is a bottom-up answer. But if we then ask why are its genes as such we would discover that they are a product of evolution which is selected for the color best suited to that environment. The polar environment is white so the genes have produced a white bear that have been selected for that environment. If the bear was in the Canadian forest it would be a brown bear instead. Thus we see downward causation acting with the cause coming from the environment to affect the state of the individual. Likewise the specific atomic nuclear interaction in the interior of the star at any given time is determined by where the reaction is taking place within the overall star. Thus the overall structure of the system is affecting the specific phenomena in a downward fashion. But again the micro level atomic interactions affect the whole creating a nonlinear causal relationship between the system's micro and macro levels with feedback between them. These patterns have downward causal efficacy in that they can affect which causal powers of their constituent elements are activated and this has significant implications for our conception of determinism. Both top-down and bottom-up causation can occur at the same time. Whereas linear causality and upward causality lead to the vision of a deterministic world. Nonlinear causality leads to a greater capacity for indeterminism. Indeterminism is the concept that events are not caused or not wholly caused deterministically by prior events. With linear causality a past cause creates a current effect in a single direct fashion. With nonlinear causality a cause may create an effect but because of the top-down bottom-up bidirectional flow to causality the context and conditions of this low level interaction are conditioned by higher level phenomena. Meaning the overall outcome is a product of this more complex interaction between the lower level cause and effect interaction and the upper level organization that sets the context for that interaction. Thus enabling a much greater opportunity for indeterminism. The philosopher Robert van Gullig describes this phenomena as such. A given physical constituent may have causal powers but only some subset of them will be activated in a given situation. The larger context i.e. the pattern of which it is a part may affect which of its causal powers get activated. Thus the whole is not any simple function of its parts since the whole at least partially determines what contributions are made by its parts. Thus with nonlinear causality the cause of events are not directly determined by preceding events but more emerge out of the bidirectional exchange between the conditions set by the overall system and the local level interactions. With nonlinear causality a single cause may have many effects such as a nerve cell sending out many impulses or inversely many causes may have a single effect such as a hurricane being a product of temperature pressure humidity etc. This leads to the idea of equifinality which is the principle that in open systems a given end state can be reached by many potential means. The term and concept are derived from Hans Dreisch the development biologist and later applied by Ludwig von Bertelenfie the founder of general systems theory. Some systems have more than one pathway or process for achieving a given goal. This increases the likelihood that the system will achieve its goal under various environments and circumstances. If one subsystem is damaged or if environmental circumstances change significantly the presence of multiple mechanisms or pathways thereby increases the likelihood that the varying condition can be adapted to or overcome. For example the human immune system has both a pre-existing component and induced antibody component to respond to foreign invaders. Some organisms have multiple pathways and use them in different environmental circumstances. These redundant systems accomplish the same end but do so with more than one similar or equivalent channel. Our conception of time is closely connected to our understanding of causality. A linear conception of causality leads to a unidirectionality to cause an effect over time and this is a fundamental component in structuring our understanding of past present and future. When considering only matter and energy within a purely physical system this unidirectionality to causality with respect to time may hold. However when we introduce information into the model it now becomes possible for future events to feedback to affect current events and thus enable reverse causation. Information encoding and processing is an essential feature of biological systems that differentiates them from purely physical systems and allows this departure from deterministic physical causality. Many entities have control systems that enable them to process information. Examples include animals, humans, social institutions and various kinds of technology. With a control system the structure and initial conditions may not matter. What matters is the goal. Future goals can determine current actions. Here we can note how causality is reversing as it goes from some projected future event back to affect the present state. The human body is acting out this process virtually every moving second in that we typically formulate some goal before initiating any action such as desiring the future state of eating a meal before acting out the process of cooking him. The physical structure and initial conditions determine the outcomes as governed by equations. Past effects cause current events going forward but with the control system the information defines a systems desired behavior or response and thus cause and effects are contingent on higher level information whose cause is derived from some future projection.