What can we learn about complex systems using agent-based models and simulations?

1. What can we learn about complex systems using agent-based models and simulations?

2. Complex Systems Concepts • Emergence: • Dynamic and static patterns; • Part-whole relations; • Self-*/Auto-* phenomena, e.g.: • Self-organisation; • Autopoiesis; • Self-replication.

3. Two types of computational modelling • Modelling categories of phenomena through formalisation e.g. by saying exactly what self-organisation is in terms of its information dynamics i.e. defining the criteria that have to be fulfilled for us to say we have an instance of self-organisation. • Modelling and simulating concrete phenomena that happen to exhibit/exemplify self-organisation, feedback, autopoeisis etc.

4. Agent-based modelling (ABM) • The ABM framework is itself a model (in the first sense in the previous slide) of a system composed of entities that can interact/share information with each other. • A particular ABM is a model (in the second sense in the previous slilde) of a specific target system or class of target systems where the agents represent entities in the target system and the agent rules determine which interactions and state transitions they can undergo; these represent the interactions and behaviours that can be exhibited by their entity-correspondents.

5. Why is agent-based modelling used to model complex systems? • Interesting system level phenomena (e.g. patterns, behaviours) can `emerge’ from the interactions between agents in the simulation. • These system level phenomena are difficult (some have argued, impossible) to derive analytically from the agent rules alone. • The information dynamics of emergence and self*- phenomena can be simulated.

6. Which types of hypotheses can be formulated and tested with an agent-based model and its simulations? • Agent-Conditions-System hypotheses: Agents with rules R1, R2,… Rn can give rise to system phenomena P if conditions C1, C2,… Cn hold (where conditions can be any sort of constraint e.g. number of agents, system parameters, agents having particular initial settings). • Multi-level hypotheses: Phenomena P1, P2,…Pn have a set of relations with one another (where Pi can be phenomena described at any level of abstraction).

7. What are the modelling limits? • At the agent level, we can only represent properties that can be assigned a categorical value. • All operations must be computable i.e. they have a turing machine equivalent. • Any higher level property is either (a) a composition of agent-level properties; (b) a higher order category of agent level properties or (c) both.

8. Limitations to hypotheses • Can not hypothesise about anything not built into the model - therefore all hypotheses must be about a closed system. • Can only hypothesise about categorical properties and relations that can be decided computationally/formally.

9. Apart from the Turing constraint, what’s special about computation? • Limit to accuracy in number representation. • Random generators do not generate true randomness. • Infinity can not be represented. • These all imply that for any given model, there is a finite set of computationally unique simulations (which can be expressed as unique complex event types where all the same relations hold between all the events.

10. Is ABM enough? • Is an understanding of closed systems suffficient? • Are categorical properties the only ones that matter? • Can formal operations represent all transformations? • To address these questions, we need to return to our fundamental assumptions about complex systems - we need theories of complexity. Over to you Alvaro.

11. BACK TO THE PHILOSOPHICAL FOUNDATIONS OF COMPLEXITY SCIENCE • 1975-1985 (JL Le Moigne, “Complexité” Dictionnaire de philosophie et histoire de science, PUF, Paris, 1999): the emergence of a “philosophy of complexity” until the foundation in 1984 of the Santa Fe Institute and the “science of complex systems”. Then philosophy of complexity is forgotten and displaced by complex systems modelling. • Principle new concepts philosophically discussed during these years: complexity, emergence, self-organization, autonomy, autopoiesis, dissipative structures, feed-back, strange loop, self-reference, paradox, etc. • Principle authors discussing these concepts: Ilya Prigogine, Heinz von Foerster, Henri Atlan, Jean-Pierre Dupuy, Isabelle Stengers, Douglas Hofstadter, Robert Rosen, Edgar Morin, Francisco Varela, Humberto Maturana, etc.

12. SIGNS OF A NEW PHILOSOPHICAL VIEWSIGNS OF A PARADIGM SHIFT? • From closed systems to open systems (von Bertalanffy) • From “observed” systems to “observant” systems (von Foerster) • From hetero-organized systems to “self-eco-re-organized” systems (Morin) • From alopoietic systems to autopoietic systems (Varela and Maturana) • From deterministic systems to non-deterministic systems • From linear systems to non-linear systems • From “Newtonian” systems to “anticipatory” systems (Rosen) • From “objective” systems to “subjective-objective” systems • From Physics as “guiding difference” (Luhmann) to Biology as “guiding difference” • From temporal reversibility to temporal irreversibility (Prigogine) • From organized simplicity (mechanic systems) and disorganized complexity (thermodynamic systems) to “organized complexity” (biological and social systems) • From a principle of disjunction between subject (observer) and object (observed) to a principle of conjunction between subject (observer) and object (observed): from objectivism to constructivism, from first-order cybernetics to second-order cybernetics

13. THE SCIENTIFIC AND TECHNICAL LIMITS OF PHILOSOPHY OF COMPLEXITY • We need tools and techniques to “test” theories, the new emerging concepts • Philosophy of complexity has scientific and technical limits. If the system is subjective/objective, we need both a subjective and reflexive view and an objective test to understand it. • ABM seems now a useful tool for the “objective” testing (unable or not so developed as now during the 1975-1985 period of philosophy of complexity)

14. THE NEED TO LINK THE PHILOSOPHY OF COMPLEXITY AND THE SCIENCE OF COMPLEX SYSTEMS, AGENT-BASED MODELLING, IN ORDER TO BUILD A “PARADIGM OF COMPLEXITY” Two meanings of “paradigm” in Thomas Kuhn’s work (The Structure of Scientific Revolutions, University of Chicago Press, Chicago, 1962). - A worldview shared by a scientific community - Models and examples of scientific resolutions Two ways to approach to complexity: ABM (focusing on “models and examples”) and philosophy (focusing on the “worldview”). Two limits: an epistemological limit (in ABM) and a scientific limit (in philosophy of complexity) Two needs: science of complex systems needs philosophy and philosophy of complexity needs science The loop of needs The need of the link between both ways to approach complexity to understand deeply its meaning Should we then talk about a new “paradigm of complexity” finally born?