Carlos Gershenson

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.

Self-Organizing Traffic Lights: A Realistic Simulation

We have previously shown in an abstract simulation (Gershenson in Complex Syst. 16(1):29–53, 2005) that selforganizing traffic lights can greatly improve traffic flow for any density. In this chapter, we extend these results to a realistic setting, implementing self-organizing traffic lights in an advanced traffic simulator using real data from a Brussels avenue. In the next section, a brief introduction to the concept of self-organization is given. The SOTL control method is then presented, followed by the moreVTS simulator. In Sect. 3.5, results from our simulations are shown, followed by Discussion, Future Work, and Conclusions.

Smartocracy: Social Networks for Collective Decision Making

Smartocracy is a social software system for collective decision making. The system is composed of a social network that links individuals to those they trust to make good decisions and a decision network that links individuals to their voted-on solutions. Such networks allow a variety of algorithms to convert the link choices made by individual participants into specific decision outcomes. Simply interpreting the linkages differently (e.g. ignoring trust links, or using them to weight an individuals vote) provides a variety of outcomes fit for different decision making scenarios. This paper can discuss the Smartocracy network data structures, the suite of collective decision making algorithms currently supported, and the results of two collective decisions regarding the design of the system

The Implications of Interactions for Science and Philosophy

Reductionism has dominated science and philosophy for centuries. Complexity has recently shown that interactions—which reductionism neglects—are relevant for understanding phenomena. When interactions are considered, reductionism becomes limited in several aspects. In this paper, I argue that interactions imply nonreductionism, non-materialism, non-predictability, non-Platonism, and non-Nihilism. As alternatives to each of these, holism, informism, adaptation, contextuality, and meaningfulness are put forward, respectively. A worldview that includes interactions not only describes better our world, but can help to solve many open scientific, philosophical, and social problems caused by implications of reductionism.

The World as Evolving Information

This paper discusses the benefits of describing the world as information, especially in the study of the evolution of life and cognition. Traditional studies encounter problems because it is difficult to describe life and cognition in terms of matter and energy, since their laws are valid only at the physical scale. However, if matter and energy, as well as life and cognition, are described in terms of information, evolution can be described consistently as information becoming more complex.

Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis

In recent decades, the scientific study of complex systems (Bar-Yam 1997; Mitchell 2009) has demanded a paradigm shift in our worldviews (Gershenson et al. 2007; Heylighen et al. 2007). Traditionally, science has been reductionistic. Still, complexity occurs when components are difficult to separate, due to relevant interactions. These interactions are relevant because they generate novel informationwhich determines the future of systems. This fact has several implications (Gershenson 2013).

Why does public transport not arrive on time? The pervasiveness of equal headway instability

Background The equal headway instability phenomenon is pervasive in public transport systems. This instability is characterized by an aggregation of vehicles that causes inefficient service. While equal headway instability is common, it has not been studied independently of a particular scenario. However, the phenomenon is apparent in many transport systems and can be modeled and rectified in abstraction. Methodology We present a multi-agent simulation where a default method with no restrictions always leads to unstable headways. We discuss two methods that attempt to achieve equal headways, called minimum and maximum. Since one parameter of the methods depends on the passenger density, adaptive versions—where the relevant parameter is adjusted automatically—are also put forward. Our results show that the adaptive maximum method improves significantly over the default method. The model and simulation give insights of the interplay between transport design and passenger behavior. Finally, we provide technological and social suggestions for engineers and passengers to help achieve equal headways and thus reduce delays. Conclusions The equal headway instability phenomenon can be avoided with the suggested technological and social measures.

Guiding the self-organization of random Boolean networks

Random Boolean networks (RBNs) are models of genetic regulatory networks. It is useful to describe RBNs as self-organizing systems to study how changes in the nodes and connections affect the global network dynamics. This article reviews eight different methods for guiding the self-organization of RBNs. In particular, the article is focused on guiding RBNs toward the critical dynamical regime, which is near the phase transition between the ordered and dynamical phases. The properties and advantages of the critical regime for life, computation, adaptability, evolvability, and robustness are reviewed. The guidance methods of RBNs can be used for engineering systems with the features of the critical regime, as well as for studying how natural selection evolved living systems, which are also critical.

Measuring the Complexity of Self-Organizing Traffic Lights

We apply measures of complexity, emergence and self-organization to an abstract city traffic model for comparing a traditional traffic coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only traffic is a non-stationary problem, which requires controllers to adapt constantly. Controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures, we can say that the self-organizing method achieves an adaptability level comparable to a living system.

Evolution of complexity

The evolution of complexity has been a central theme for Biology [2] and Artificial Liferesearch [1]. It is generally agreed that complexity has increased in our universe, giving wayto life, multi-cellularity, societies, and systems of higher complexities. However, the mech-anisms behind the complexification and its relation to evolution are not well understood.Moreover complexification can be used to mean different things in different contexts. Forexample, complexification has been interpreted as a process of diversification between evolv-ing units [2] or as a scaling process related to the idea of transitions between different levelsof complexity [7]. Understanding the difference or overlap between the mechanisms involvedin both situations is mandatory to create acceptable synthetic models of the process, as isrequired in Artificial Life research.Concretely, many open questions related to the evolution of complexity can be asked.Some were proposed in the call for papers, of which some are addressed in this issue:1. How could complexity growth be measured or operationalised in natural and artificialliving systems?2. How can existing data from nature be brought to bear on the study of this issue?3. What are the main hypotheses about complexity growth that can actually be testedtoday?4. Are the principles of natural selection as they are currently understood sufficient toexplain the evolution of complexity in living systems?5. What are the environmental and other constraints of the evolution of complexity inliving systems?6. What is the role of developmental mechanisms in the evolution of complexity in livingsystems?1