2 nd Module Causality and information Luis E. Bruni

1. History, Theory, and Philosophy of Science (In SMAC + RT)7th smester -Fall 2005Institute of Media Technology and Engineering Science Aalborg University Copenhagen 2nd Module Causality and information Luis E. Bruni

2. Causality as an ontological questionArthur Peacocke (Chapter 2) • “…the succession of events which form causal chains is independent of the choice of frame of reference and, indeed, the concept of causality is affected by this initial theory of Einstein only to the extent that we now have to recognize that causal influences can never be transmitted through the universe at a speed greater than that of light”. • Why? • Think about the implications of these statement. • What is causality? What are our presuppositions about causality?

3. Causality • Causality or Causation  a process linking two or more events or states of affairs so that one brings about or produces the other. • One event is the cause of another if: • (a) the event occurs prior to the effect • (b) there is an invariant conjunction of the two events • (c) there is an underlying mechanism or physical structure attesting to the necessity of the conjunction. • Since (c) is not always demonstrable in empirical data the requirement may be replaced by tests assuring that no third variable controls both or mediates between the two events. Without this weaker test, a cause may be termed spurious and genuine otherwise.

4. Aristotelian causality 1)Material the direct physical corelate. 2)Efficient mechanical workings  the agent. 3)Formal abstract forms towards which developing entities naturally progress the model. 4)Final finality  intelligibility.

5. The hierarchical nature of Aristotelian causality (I) Ex: the causality of a battle. • Material causes  soldiers and guns  affect only a subfield of the overall action. • Efficient causes  officers  their scale of involvement is most commensurate with that of the battle itself. • Formal cause  the battle’s strategy. • Final cause  the reasons of the State  a head of state influences events that extent well beyond the time and place of battle.

6. The hierarchical nature of Aristotelian causality (II) Ex: the causality of building a house. • Material causes  bricks, cement, tools. • Efficient causes  bricklayer. • Formal cause  the blueprirint  the architect’s idea. • Final cause  the family that lives in the house. The hierarchical nature of Aristotelian causality  irreconcilable with the Newtonian reductionistic and universal picture.

7. Deterministic causality • The systems’ behavior is specified without probabilities (other than zero or one) is predictable without uncertainty once the relevant conditions are known. • Deterministic systems leave nothing to chance and are of necessity lawful  there are no options. • Deterministic systems conform to the ideal of a machine in which wear and tear, mechanical failures and unreliabilities are absent. • Modern computers are conceived as deterministic machines.

8. Newtonian systems Five conceptual presuppositions of the Newtonian approach  Newtonian systems are: 1) Deterministic  given the initial position of any entity in the system, a set of forces operating on it, and stable closure conditions  every subsequent position of each particle or entity in the system is in principle specifiable and predictable. 2) Closed  they admit of no outside influences other than those prescribed as forces by Newton’s theory. 3) Reversible  the laws specifying motion can be calculated in both temporal directions.

9. Newtonian systems 4) Atomistic (strongly decomposable)  reversibility presupposes that larger units must be regarded as decomposable aggregates of stable least units  that what can be built up can be taken apart again  increments of the variables of the theory can be measured by addition and subtraction. 5) Universal  they apply everywhere, at all times, and over all scales. [Depew and Weber (1994), Ulanowicz (1997)]

10. Posibilistic causality • The systems’ behavior includes options without specification of probabilities within that system. • In contrast to deterministic systems  possibilistic systems leave some uncertainty in the specification of future states and behavior, even if all relevant conditions are known. • Possibilistic systems  also called non-deterministic.

11. Probability theory and statistics • Probability theory and statistics  had been created expressly to circumvent an observer’s ignorance about detailed events that everyone assumed were amenable to classical deterministic mechanics. • The same mathematics could be applied to events that were inherently stochastic (provide one accepts indeterminacy in a world of Newton’s law). • Since the advent of quantum physics  growing credibility has been accorded to indeterminacy over ignorance as the proper object of statistical considerations.

12. The Laplaceam Demon "We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes." Pierre-Simon Laplace

13. Different kinds of systems • Deterministic systems. • Non-deterministic systems. • Stochastic systems  combined a random component with a selective process. • Teleonomic  teleological systems  goal-oriented behaviour  “self-organised” systems.

14. Predictibility • Social and cultural events  informational, semiotic, mental, or cognitive processes  are rarely uni-causal phenomena and as deterministic as in the natural sciences. • Causality in the social sciences therefore tends to be multi-causal and probabilistic  as in information theory. • Predictability  the theoretical importance of causal explanations is that one can apply them to explain what happened and predict what will happen. • Their practical importance is that they lead one to produce or to prevent causally related events by direct or indirect intervention.

15. What is it that probabilities measure? • There has been a shift in attitude  from “probabilities measure our ignorance about a deterministic situation”  quantitative epistemology  to “probabilities reflect an indeterminacy inherent in the process itself  it bears also upon the ontological character of events. • This shift has not permeated Information Theory  the central concept “uncertainty”  a state of knowledge, not a state of nature. • Information Theory  quantifies changes in probabilities. • Information  anything that causes a change in probability assignment.

16. Shannon’s information • If an event changes its probability assignment from 50-50 to 70-30  from more to less indeterminate  there is information engendered by whatever caused the bias (the change in probability assignment). • Remember  information = anything that causes a change in probability assignment. • Shannon’s formula may be useful for quantifying those factors that help constrain flows along certain preferred pathways. • Whatever constraints partition the flows (of events) in the observed proportions  they reduce the indeterminacy  they inform the system.

17. Information  imparts order • We always begin work on a problem with some degree of uncertainty  through repeated observations under different conditions we reduce that uncertainty  gain information  however under all possible circumstances a residual “uncertainty” will persist due to the inherent indeterminacy in the process and its context. • The term “uncertainty” is frequently replaced by “indeterminacy”. • Information  refers to the effects of that which imparts order and pattern to a system. • Uncertainty = information  has been very confusing. • The confusion comes from a failure to distinguish between “information” and “information capacity”  Capacity of a system for either information or indeterminacy  order or disorder.

18. Cybernetic information • Some think it always necessary to identify a sender, a receiver, and a channel over which information flows. • Information theory transcends communication  probabilities are its fundamental elements. • What about the semantic value of information?

19. Material  Mechanical • Decartes  mechanical aspects of nature. • Thomas Hobbes all reality is in essence material  including God and the human soul. • End of XVII century  material + mechanical • We have on the one side material-mechanical causality.

20. … andbeyond? • It is normally (but not universally) assumed that events at any hierarchical level are contingent upon (but not necessarily determined by) material elements at lower levels. • What kind of causality is implied in informational, semiotic, mental, or cognitive processes?  as in culture? • If we base culture on digital media  does that make cultural processes more deterministic? Or more probabilistic in the sense of Information Theory?

21. News of a difference • The smallest unit of information is a difference or distinction, or news of a difference. • A sign  an idea  a complex aggregate of differences or distinctions • More elaborate signs and ideas can be formed by complex aggregates of differences  emerging codes.

22. Information vs. Impacts • Information  a difference that makes a difference to a system capable of picking it up and reacting to it  for there to be a “difference” - news of a distinction - there has to be a biological system that senses it. • Otherwise they would not be differences, they would be just impacts  think of a receptor. • So information means a difference that makes a difference to some system with interpretative capacity

23. Smoke Fire Let´s get out of here What is a sign? • A sign is something that stands for something to some system with capacity for interpretation

24. Differences and purpose • “The number of potential differences in our surroundings ... is infinite. Therefore, for differences to become information they must first be selected ...” and categorised by an interpretative system with such capability of pattern recognition. • Differences are not intelligible in the absence of a purpose.

25. Informational, Semiotic, Mental and Cognitive Processes • Two types of causal links • 1) “pleroma” (Bateson) • the world of non living billiard balls and galaxies • the material world • where forces and impacts are the “causes” of events • 2) “creatura” • the world of the living • where distinctions are drawn and a difference can be a cause • the equivalent of cause is information or a difference

26. Information is always contextual, and contextis always hierarchical.

27. History, Theory, and Philosophy of Science (In SMAC + RT)7th smester -Fall 2005Institute of Media Technology and Engineering Science Aalborg University Copenhagen 2nd Module Causality and information Luis E. Bruni