Causation, Control, and the Evolution of Complexity H. H. Pattee Synopsis Steve B. 3/12/09

Value of Causation? Is causation a useful concept? –Author questions the importance in causation. –Our natural language drives us to casually explain events. Why? Because. Taught that given one proximal cause, it is rude to continue to ask, why? But language leads to ambiguity of causal forms Naïve causation requires a direction in time –Concept of causation presupposes a model of time Tempted to decompose into a simple string of ordered events (one dimensional) High-dimensional and diffuse concurrent influences are seldom viewed as causes –Further analysis shows that causation is context or model dependent Not consistent between contexts Causation is gratuitous in modern physics –Reducing causation to microscopic physical laws does not have fundamental explanatory value Does not handle real world cases of multiple interdependent forces. Does lead to an understanding of the fundamental problem –Microscopic equations of physics are time symmetric and conceptually reversible. Causation is intrinsically irreversible

Causation - Direction in Time? Do statistical laws give a direction in time? –Concepts of causation have different meaning is statistical and deterministic models –Reductionists assume that cause refers to events at a lower level model Measurement gives a direction in time –You may not measure at the lower level, but by averaging at the higher level. cause of temperature –May be able to only measure the end state flipping a coin –The asymmetric direction of time is not the microscopic behavior of the system bot the higher level measurement process. –Our concept of the direction of time (concepts of causation) arise from our being and observer of events. Not from the events themselves. Universal causes (like reductionists) are not explanatory –Reducing to a low level model does not lead to a useful understanding. Give no clue to the level of observables necessary for a useful model –Author believes that only our familiarity with linguistic form leads us to accept uncritically universal causes as explanations.

When does “Cause” become useful? Complementary models require complementary causes –Two fundamental levels of physical models are complementary microscopic laws & statistical laws –Complementary models are formally incompatible but both are necessary deterministic cause and statistical cause (or complete chance) –either can invoke causal “explanations” of events –Extreme forms and model-dependencies of determinism and stochastic are combined as emergent events that require new levels of description Useful causation requires control –Useful when the cause of an event might be controllable. –The value of the concept of causation lies in its identification of where our power and control can be effective. Cannot control the laws of physics –Looking for focal events over which we might have had some control. Not looking for the multitude of necessary conditions (that are uncontrollable) But for a focal event that might have prevented the result –Examples: Malaria, Scurvy, an Aircraft crash, or turning on a light

Control The origin of control –Causation is useful only when associated with power and control –Pragmatic control (and causation) requires some measure of utility –Example: living organisms Upward causation from genes - control is local, sequential and easy to model –heritable - stored in simple, localized, semiotic memory Downward causation from organism level - control is diffuse, parallel and complex –not stored - part of time-dependent dynamics of the phenotype Levels of control match models of causation –Different levels of causation will be associated with different levels of control –Downward causation describes the collective, concurrent, distributed behavior at a level where control is not practical Evolution requires semiotic control of construction –Semiotic description can be read two ways: syntactically to be transmitted semantically to control construction –The fundamental requirement for open-ended evolvability: The interdependence of the semiotic domain of the heritable genetic memory and the dynamic domain of the construction and function.

Dynamic Vs. Semiotic Artificial dynamics and self-organization –Genetic control & natural selection vs. self-organizing nonlinear dynamics sequential semiotic rules vs. distributed, coherent neural dynamics. –These are intricately related at all levels of organization. –Semiotic and dynamic models are complementary –Dynamic models describe how events change in time state determined - no memory –Semiotic models based on discrete symbols and syntactic rules memory is fundamental When is downward causation a useful concept? –Not in all cases (example: natural selection, simple statistical physics) –Only when it identifies the controllable observables of a system or suggests a new model of the system that is predictive. –Life and the evolution of complex systems is based on the semantic closure of semiotic (upward) and dynamic (downward) controls. Semiotic - discrete, local, & rate-independent Dynamic - continuous, distributed and rate-dependent –There exists a necessary mapping between these complementary models

Conclusion To understand life, it is essential to distinguish two complementary types of control models. –Semiotic model Exerts upward control from local isolated memory Explains how control can be inherited Provides efficient search process for discovering adaptive & emergent structures –Dynamic model Downward control from global network of coherent, interactive components. How many components can be integrated in the course of development and coordinated into emergent functions –Neither model has much explanatory value without the other By themselves, can only account for limited level of self-organization. Evolving complex systems requires the coupling of both self-organizing processes Without both, very limited emergent and survival potential

Final Impression Author believes the use of causation at the level of physical laws is only a gratuitous manner of speech with no fundamental explanatory value. It is tempting to jump to identify of the cause of something. If you do not look at the proper context, the right level, and/or the appropriate perspective, you can be lead to the wrong conclusions. In addition, complex systems cannot be simplified to a single view of the cause. There are complimentary causes that need to be understood. Identification of cause needs to be useful and explanatory Meaningful cause needs to be related to some level of control –If causation does not identify controllable events or actions, it is an empirically gratuitous linguistic form that is so universal that it results in nothing but endless philosophical controversy.

Questions for Consideration How do you determine when you are at the correct level to understand self-organizing systems? If you focus on one cause, in one context, will that cause be meaningful in another context? Is there a relationship to Nature vs. Nurture arguments?