Francis Heylighen

Cybernetics and Second Order Cybernetics

I. Historical Development of Cybernetics 1 A. Origins 1 B. Second Order Cybernetics 2 C. Cybernetics Today 4 II. Relational Concepts 5 A. Distinctions and Relations 5 B. Variety and Constraint 6 C. Entropy and Information 6 D. Modelling Dynamics 7 III. Circular Processes 8 A. Self-Application 8 B. Self-Organization 9 C. Closure 10 D. Feedback Cycles 11 IV. Goal-Directedness and Control 12 A. Goal-Directedness 12 B. Mechanisms of Control 13 C. The Law of Requisite Variety 14 D. Components of a Control System 15 E. Control Hierarchies 17 V. Cognition 18 A. Requisite Knowledge 18 B. The Modelling Relation 19 C. Learning and Model-Building 20 D. Constructivist Epistemology 22 Bibliography 23

Collective Intelligence and its Implementation on the Web: Algorithms to Develop a Collective Mental Map

Collective intelligence is defined as the ability of a group to solve more problems than its individual members. It is argued that the obstacles created by individual cognitive limits and the difficulty of coordination can be overcome by using a collective mental map (CMM). A CMM is defined as an external memory with shared read/write access, that represents problem states, actions and preferences for actions. It can be formalized as a weighted, directed graph. The creation of a network of pheromone trails by ant colonies points us to some basic mechanisms of CMM development: averaging of individual preferences, amplification of weak links by positive feedback, and integration of specialised subnetworks through division of labor. Similar mechanisms can be used to transform the World-Wide Web into a CMM, by supplementing it with weighted links. Two types of algorithms are explored: 1) the co-occurrence of links in web pages or user selections can be used to compute a matrix of link strengths, thus generalizing the technique of &201C;collaborative filtering&201D;; 2) learning web rules extract information from a user&2018;s sequential path through the web in order to change link strengths and create new links. The resulting weighted web can be used to facilitate problem-solving by suggesting related links to the user, or, more powerfully, by supporting a software agent that discovers relevant documents through spreading activation.

When Can We Call a System Self-Organizing?

We do not attempt to provide yet another definition of self-organization, but explore the conditions under which we can model a system as self-organizing. These involve the dynamics of entropy, and the purpose, aspects, and description level chosen by an observer. We show how, changing the level or “graining” of description, the same system can appear self-organizing or self-disorganizing. We discuss ontological issues we face when studying self-organizing systems, and analyse when designing and controlling artificial self-organizing systems is useful. We conclude that self-organization is a way of observing systems, not an absolute class of systems.

Selection of Organization at the Social Level: obstacles and facilitators of metasystem transitions

This paper examines in how far Turchins concept of metasystem transition, as the evolutionary integration and control of individual systems, can be applied to the development of social systems. Principles of collective evolution are reviewed, and different types of competitive or synergetic configurations are distinguished. Similar systems tend to get involved in negative sum competition, and this precludes optimization at the group level. The development of shared controls (e.g., through conformist transmission) may overcome the erosion of group level cooperation, and thus facilitate the emergence of a division‐of‐labor organization. The resulting social metasystem transition is exemplified by the emergence of multicellularity, insect societies and human sociality. For humans, however, the on‐going competition between the cooperators produces an ambivalent sociality, and a weakly integrated social metasystem. Strengths and weakhesses of the main social control mechanisms are reviewed: mutual monitoring,...

Causality as distinction conservation: a theory of predictability, reversibility, and time order

“Equal causes have equal effects” is reformulated by defining causality as a distinction-conserving relation. Unpredictable, respectively irreversible, processes are analyzed as processes in which distinctions are created, respectively are destroyed. Different types of partially causal and pseudo-causal relations are examined. Time order is derived from distinction conservation. It is argued that the emergence of macroscopic distinctions and causal relations is due to a self-organizing evolution, characterized by natural selection. The relationship between “physical” and “observer-dependent” factors in determining causal relations is discussed.

Hebbian algorithms for a digital library recommendation system

This paper proposes a set of algorithms to extract metadata about the documents in a digital library from the way these documents are used. Inspired by the learning of connections in the brain, the system assumes that documents develop stronger associations as they are more frequently co-activated. Co-activation corresponds to consultation by the same user, and decreases exponentially with the time interval between consultations. The strength of activation is proportional to the users interest for the document, either evaluated explicitly, or inferred implicitly from user actions or the duration of the consultation. Co-activation values are added, producing a matrix of associations. This matrix can be used to recommend the documents that are most strongly related to a given document, most relevant to the users implicit interest profile, or most interesting to users overall. Moreover, it allows the calculation of document similarity values, which in turn can be used to cluster similar documents. The data needed to feed such a recommendation system are readily extracted from the usage logs of document servers, and can be processed either in a centralized or a distributed manner.

(Meta)systems as constraints on variation— a classification and natural history of metasystem transitions

A new conceptual framework is proposed to situate and integrate the parallel theories of Turchin, Powers, Campbell and Simon. A system is defined as a constraint on variety. This entails a 2 × 2 × 2 classification scheme for “higher‐order” systems, using the dimensions of constraint, (static) variety, and (dynamic) variation. The scheme distinguishes two classes of metasystems from supersystems and other types of emergent phenomena. Metasystems are defined as constrained variations of constrained variety. Control is characterized as a constraint exerted by a separate system. The emergence of hierarchical systems is motivated by evolutionary principles. The positive feedback between variety and constraint, which underlies the “branching growth of the penultimate level,” leads to the interpretation of metasystem transitions as phases of accelerated change in a continuous evolutionary progression toward increasing variety. The most important MSTs in the history of evolution are reinterpreted in this framewor...

Self-organization in Communicating Groups: The Emergence of Coordination, Shared References and Collective Intelligence

Complex adaptive systems consist of a large number of interacting agents. Agents are goal-directed, cognitive individuals capable of perception, information processing and action. However, agents are intrinsically “bounded” in their rational understanding of the system they belong to, and its global organization tends to emerge from local interactions, resulting in a coordination of the agents and their actions. This coordination minimizes conflict or friction, while facilitating cooperation or synergy. The basic mechanism is the reinforcement of synergetic interactions and the suppression of conflictual ones. As a result, the system as a whole starts to behave like an integrated cognitive “superagent”. The author presents several examples of this process of spontaneous coordination that leads to distributed cognition, including the emergence of a shared vocabulary, the development of standard referential expressions, the evolution of transmitted ideas (memes) towards more stereotypical forms, and the aggregation of diverse experiences into collective decisions, in which the system as a whole is more intelligent than its individual components. These phenomena have been investigated by means of multi-agent computer simulations and social psychological experiments.

Stigmergy as a universal coordination mechanism I

The concept of stigmergy has been used to analyze self-organizing activities in an ever-widening range of domains, including social insects, robotics, web communities and human society. Yet, it is still poorly understood and as such its full power remains underappreciated. The present paper clarifies the issue by defining stigmergy as a mechanism of indirect coordination in which the trace left by an action in a medium stimulates subsequent actions. It then analyses the fundamental concepts used in the definition: action, agent, medium, trace and coordination. It clarifies how stigmergy enables complex, coordinated activity without any need for planning, control, communication, simultaneous presence, or even mutual awareness. The resulting self-organization is driven by a combination of positive and negative feedbacks, amplifying beneficial developments while suppressing errors. Thus, stigmergy is applicable to a very broad variety of cases, from chemical reactions to bodily coordination and Internet-supported collaboration in Wikipedia.

Bootstrapping knowledge representations: from entailment meshes via semantic nets to learning webs

The symbol‐based epistemology used in artificial intelligence is contrasted with the constructivist, coherence epistemology promoted by cybernetics. The latter leads to bootstrapping knowledge representations, in which different parts of the system mutually support each other. Gordon Pask’s entailment meshes are reviewed as a basic application of this approach, and then extended to entailment nets: directed graphs governed by the “bootstrapping axiom”, determining which concepts are to be distinguished or merged. This allows a constant restructuring of the conceptual network. Semantic networks and frame‐like representations can be expressed in this scheme by introducing a basic ontology of node and link types. Entailment nets are then generalized to associative networks with weighted links. Learning algorithms are presented which can adapt the link strengths, based on the frequency with which links are selected by hypertext users. It is argued that such bootstrapping methods can be applied to make the World Wide Web more intelligent, allowing it to self‐organize and support inferences.