Guided self-organization.

Typically, self-organization is defined as the evolution of a system into an organized form in the absence of external pressures. A broad definition of self-organization is given by Haken (2006). “A system is self-organizing if it acquires a spatial, temporal, or functional structure without specific interference from the outside. By ‘specific’ we mean that the structure or functioning is not impressed on the system but that the system is acted upon from the outside in a nonspecific fashion. For instance, the fluid which forms hexagons is heated from below in an entirely uniform fashion and it acquires its specific structure by self-organization.” Another definition is offered by Camazine et al. (2001) in the context of pattern formation in biological systems. “Self-organization is a process in which pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the system. Moreover, the rules specifying interactions among the system’s components are executed using only local information, without reference to the global pattern.” These definitions capture three important aspects of self-organization. First, it is assumed that the system has many interacting components and advances from a less organized state to a more organized state dynamically over some time, while exchanging energy, matter, and/or information with the environment. Second, this organization is manifested via global coordination and the global behavior of the system is a result of the interactions among the agents. In other words, the global pattern is not imposed on the system by an external ordering influence (Bonabeau et al., 1997). Finally, the components, whose properties and behaviors are defined prior to the organization itself, have only local information and do not have knowledge of the global state of the system—therefore, the process of self-organization involves some local information transfer (Polani, 2003; Lizier et al., 2008). Self-organization may seem to contradict the second law of thermodynamics that captures the tendency of systems to disorder. The “paradox” was explained in terms of multiple coupled levels of dynamic activity within the Kugler–Turvey model (Kugler and Turvey, 1987): self-organization and loss of entropy occurs at the macrolevel while the system dynamics on the micro-level (which serves as an entropy “sink”) generates increasing disorder. Kauffman (2000) suggested that the underlying principle of selforganization is the generation of constraints in the release of energy. According to this view, the constrained release allows for such energy to be controlled and channeled to perform some useful work. This work in turn can be used to build better and more efficient constraints for the release of further energy and so on. Adding and controlling constraints on self-organization opens a way to guide it in a specific way. In general, one may consider different ways to guide the process (dynamics) of self-organization, achieving a specific increase in structure or function within a system. This guidance may be provided by limiting the scope or extent of the self-organizing structures/functions, or specifying the rate of the internal dynamics, or simply selecting a subset of all possible trajectories that the dynamics may take. The formal definition of guided selforganization and its properties (robustness, adaptability, scalability, etc.) remains an elusive task but there were a few recent attempts, specifically within information theory and dynamical systems: universal utility functions (Klyubin et al., 2005), informationdriven evolution (Prokopenko et al., 2006a; 2006b), robust overdesign (Ay et al., 2007), reinforcement-driven homeokinesis (Martius et al., 2007), predictive information based homeokinesis (Ay et al., 2008), etc. However, the lack of agreement of what is meant by complexity, constraints, etc., and a common methodology across multiple scales leaves any definition of (guided) self-organization somehow vague, indicating a clear gap (Polani, 2007). Filling this gap and finding new and systematic ways for the guidance of selforganization is the main theme of GSO Workshops, and the works collected in this special issue aim to identify essential guiding principles. The perspective by Polani (2009) argues that information (defined as a reduction in uncertainty, i.e., Shannon information) is a critical resource for biological organisms and that it trades off with the available metabolic energy. This leads to the parsimony principle suggesting that if organisms would develop a suboptimal information processing strategy, this would lead to a waste of metabolic energy. The parsimony principle captures the amount of information necessary to achieve a particular utility and aims to provide an implicit measure of the cost per time required to process the sensoric information for generating a desired behavior: “an organism that realizes an evolutionarily successful behavior will at the same time attempt to minimize the required sensoric information to achieve this behavior.” Polani also discusses other information-theoretic principles as candidates for understanding the information dynamics of organisms, concluding that information may be a fundamental currency underlying the HFSP Journal E D I TO R I A L