Guided Self!Organization:" from chaos to planning?! Francis Heylighen! Evolution, Complexity and Cognition Group! Vrije Universiteit Brussel! Newtonian Science! Making simple models, by means of:# •!Analysis! •!Reductionism! •!Determinism! •!Reversibility! Complexity! complexus = entwined# •!Many parts! •!that are connected so that they are di"cult to separate! In between Order and Disorder# •!the #edge of chaos$! From Newton to Complexity! Analysis → Connection# Reductionism → Emergence# •! The whole is more than the sum of its parts! Deterministic change → Uncertainty# •! We cannot predict exactly what will happen! Matter → Organization# •! Systems more important than elements or particles! Reversible movement → Evolution# •! Creativity and progress! Emergence! whole > sum of the parts# Whole has emergent properties # •! properties not reducible to properties of the parts! examples# •!car: maximum speed = emergent, weight = reducible! •!music: melody = emergent, volume= reducible! Open Systems! Inseparable from environment:# •!input = incoming %ows of matter, energy, information! •!output = outgoing %ows! SYSTEEM input throughput output grens OMGEVING Coupling! di$erent systems can be coupled via their input and output# three basic types# sequentiëel parallel circulair Networks! Many coupled systems form a network# Which can be seen as a system itself# Subsystems and Supersystems! subsystemen supersysteem Hierarchy! Linearity! Linearity = e$ect proportional to cause# •!using k times as much raw material → & produces k times as much output! •! f(k.a) = k.f(a) Therefore: Small causes ! small effects Large causes ! large effects Non'linearity! E$ect can be much larger or much smaller than cause# Usually caused by e$ect feeding back into cause# # positive feedback negative feedback! amplifying deviations suppressing deviations ! → unstable → stable # Chaos! sensitive dependence on initial conditions# •!Unobservably small perturbations can produce huge e(ects! the butter%y e$ect: %apping of wings may create a tornado! → intrinsic unpredictability and uncontrollability# Self'organization! Spontaneous emergence of order or organization# •!not imposed by outside or inside agent! Organization distributed over all the components# •!Collective, decentralized! •!Robust against disturbances! Mechanisms of self'organization! Global order emerges from local interactions# •!Components initially interact only with their neighbors! •!Until they #align$! •!i.e. )nd a stable, #&t$ arrangement! •!Alignments tend to propagate to further neighbors! •!until they spread over the system as a whole! Magnetization! Magnetization! B*nard convection! surface surface bottom (hot) bottom (hot) B*nard convection! Dynamics of Self'organization! Local variation ! Selective elimination of the #un)t$ arrangements! Growth of the )t arrangements! •!Positive feedback! Suppression of deviations from that arrangement! •!Negative feedback! Result: Initially rapid growth → stabilization → robustness and continuing adaptation# Attractor Dynamics! Non!linear systems typically have several attractors# Attractor=# •!!stable region in state space! •!that the system can enter& but not leave! •!surrounded by basin! •!System descends & spontaneously from & basin into attractor! •!and comes back to the & attractor if forced out of it! Order out of Chaos! Self!organization is stimulated by random variation 'noise, chaos(# •! more con)gurations are explored in the same time interval! •! higher probability of )nding a #deep$ attractor ! •! therefore, a stable outcome is reached more quickly and reliably! Example: shaking! Throw cubes randomly in a box# •!preference for position close to bottom! •!But cubes mutually obstruct → #friction$! Shaking reduces volume# •!Random variation explores many possible con)gurations! •!But preferentially retains the most stable ones at the bottom! Complex Adaptive Systems! Collection of interacting agents# Characterized by self"organization# •! global coordination and adaptation! •! emerging from local interactions! Examples# •! Societies, groups, communities! •! Cities, regions! •! Ecosystems, markets! •! World'Wide Web ! Agent! Goal!directed individual# •! tries to maximize #)tness$ ! Follows condition!action rules # •! if condition A sensed, then perform action B! •! A → B! •! where B in general increases )tness! Agents are diverse! Di$erent agents have in general di$erent abilities:# •! Knowledge of rules! •! Information about conditions! •! Capacity to perform actions! Bounded rationality! Agents have only # •! local information! •! Limited information processing ability! The environment is complex and unpredictable# •! non'linearity may give rise to chaos! Therefore, agents cannot predict the long!term e$ects of their actions# •! they must rely to some degree on trial'and'error! •! They must constantly adapt to the circumstances! Evolution of Synergy! Agents co!evolve# •!Variation and selection lead to mutual adaptation! •!Each acts so as )t in with its neighbors, and vice versa! Selected interaction characterized by# •!Minimal friction +con%ict, negative sum, ! •!Maximal synergy +#win'win$, positive sum,! Leads to stable )connection* or )alignment* # •!No more con%ict, but cooperation! Propagation of Alignment! More agents join alignment# •!Often ampli)ed by positive feedback! •!Larger aligned assembly exerts larger in%uence on non' aligned agents! •!Pressure to #join$ increases! Eventually, the whole system becomes aligned# All agents act synergetically# Emergence of & collective intelligenc"e Distributed cognition# •! di(erent agents contribute di(erent results at di(erent times and places! •! results are integrated! •! together, they solve the global problem:! •!co!ective inte!igence! Coordination mechanisms! Alignment of targets# minimizing con%ict or friction! Division of labor# Di(erentiation or specialization! Work%ow# Actions performed in right sequence! Aggregation of results# •!Into collective solution! Coordination! workflow division of labor aggregation Self'organization of coordination! Stigmergy# •!Result of action stimulates performance of subsequent action! •!Right agent incited to intervene at right moment! •!Aggregation into shared workspace! Reinforcement learning# •!Successful sequences of actions are reinforced! •!Unsuccessful ones are eliminated! Planning?! Planning is intrinsically di+cult for complex systems# •!Uncertainty, chaos, autonomy, openness….! Better: guided self!organization# •!Formulate desired outcomes! •!Let system itself evolve towards these targets! •!While adapting to any unforeseen complications! Don,t try to rigidly control the process!# Anticipating outcomes! Self!organizing systems evolve towards attractors# Attractors are complex and emergent# But can be foreseen to some degree# •!By using multi'agent simulations! •!Or by mapping out positive and negative feedback loops! •!Systems dynamics models! This allows us to target desired attractors# •!and avoid undesired ones! Controlling Emergent Outcomes! How can we make sure to get the desired result?# Initial situation# # problem to be dealt with by the process! Desired outcome# #situation that solves the problem! We need to stimulate moves in the right direction! But not impose a trajectory! Controlling direction! De&ne &tness criterion: # •!what is better or worse?! Reinforcement# • reward moves toward )tter situation! •!e.g. Subsidizing education or sustainable development! • inhibit moves that reduce )tness! •!e.g. Taxing pollution or waste ! Controlling boundary conditions! Set up architectural )sca$old*# Constraining structure# Make #right$ moves easy! Make #wrong$ moves di"cult! But leave enough space for exploration!! Examples# #Organs$ grown from cells! seeded on arti)cial frame! Stigmergy! Actions leaves a trace in a shared workspace# The trace stimulates agents to perform additional actions! Very robust way to collaborate# •!No need for planning or explicit coordination# •!Division of labor and work%ow emerge spontaneously# •!agents do not need to communicate! •!workspace stores and aggregates contributions! •!Successful contributions grow through positive feedback! Stigmergy: Wikipedia! Person A writes text on topic X# •!Action leaves trace on website! Person B independently reads text# Person B is stimulated to add to the text# Further contributions are aggregated on the same webpage! Positive feedback: # •!more edits → better text → more readers → more edits → ...! Stigmergy: termite hill construction! individual termite drops mud randomly! new termite attracted to add mud to already present mud! positive feedback: mud → more mud! the mud heap grows into a column! columns grow towards each other! → cathedral'like structure with arches# Stigmergy: Ant trails! Ants coming back from food source leave pheromone trace# Ants searching for food preferentially follow pheromone trace# •!deviate from trace occasionally! •!Allows them to )nd new food source! Strong trails get reinforced as more ants use them# Trails to exhausted food sources evaporate# Outcome: adaptive network of trails connecting food sources and nest in most e+cient way# Adaptation to disturbances! Sca(olding Stigmergy! A stigmergic medium can be pre!structured, e.g. :# •!Encyclopedia with templates +#stubs$, for missing articles! •!Plastic sca(old to grow an ear! •!City with some roads and landmarks, but no buildings yet! This can incite and facilitate desired self!organization# •!Attractive empty slots invite developments! •!Positive feedback: development → more development! •!Remains adaptive to unforeseen situations! Conclusion! Complexity precludes accurate prediction and control# •! → planning intrinsically unreliable! Self"organization produces robust, adaptive systems# •!Even in very complex, unpredictable environments! •!But outcomes may not be as desired! We need to investigate guided self"organization# •!Using reinforcement signals and sca(olds ! •!to steer stigmergic coordination in the desired direction!.