Toward a formalization of emergence

A Meta-Agent Based Approach to Exploit the Collective Product of Mobile Cyber-Physical Collectives

A cyber-physical system (CPS) is a system with integrated computational and physical abilities. Deriving the notion of cyber-physical collective (CPC) from a social view of CPS, we consider the nodes of a CPS as individuals (agents) that interact to overcome their limits in the collective. When CPC agents are able to move in their environment, the CPC is considered as a Mobile CPC (MCPC). The interactions of the agents give rise to the appearance of a phenomenon collectively generated by the agents of the CPC that we call a collective product. This phenomenon is not recorded as “a whole” in the CPC because an agent has only a partial view of its environment. This paper presents COPE (COllective Product Exploitation), an approach that allows one MCPC to exploit the collective product of another one. The approach is based on the deployment of meta-agents in both systems. A meta-agent is an agent that is external to a MCPC but is associated with one of its agents. Each meta-agent is able to monitor the agent with which it is associated and can fake its perceptions to influence its behavior. The meta-agents deployed in the system from which the collective product emerges provide indicators related to this product. Utilizing these indicators, the meta-agents deployed in the other system can act on the agents in order to adapt the global dynamics of the whole system. The proposed coupling approach is evaluated in a “fire detection and control” use case. It allows a system of UAVs to use the collective product of a network of sensors to monitor the fire.

Goals as Emergent Autopoietic Processes

While the phenomena of reaching a goal is generally represented in the framework of optimization, the phenomena of becoming of a goal is more similar to a "self-organization and emergent" rather than an "optimization and preexisting" process. In this article we provide a modeling framework for the former alternative by representing goals as emergent autopoietic structures. In order to conceptually situate our approach, we first review some of the most remarkable attempts to formally define emergence, and identify that in most cases such definitions rely on a preexisting system to be observed prior and post emergence, being thus inadequate for a formalization of emergent goals corresponding to the becoming of a systems as such (e.g. emergence of life). Next, we review how an implementation of the reaction networks framework, known as Chemical Organization Theory (COT), can be applied to formalize autopoietic structures, providing a basis to operationalize goals as an emergent process. We next revisit the definitions of emergence under the light of our approach, and demonstrate that recent taxonomies developed to classify different forms of emergence can be naturally deduced from recent work aimed to explain the kinds of changes of the organizational structure of a reaction network.

Engineering the emergence of norms: a review

Complex systems often exhibit emergent behaviour, unexpected macro-level behaviour caused by the interaction of micro-level components. In multiagent systems, these micro-level components may be autonomous agents and the emergent behaviour may be expressed as norms—patterns of behaviour that arise among the agents in response to their environment and each other. These emergent norms may be beneficial (e.g. by encouraging cooperative behaviour), or detrimental, but in either case it is useful to recognize these norms as they emerge and either encourage or discourage their establishment. We term this process engineering the emergence of norms and have identified three steps: the identification of a possible norm, evaluation of its benefit and its encouragement (or discouragement). This paper is an attempt to provide a survey of existing research related to these steps. We also provide an analysis of the approaches based upon their suitability for a variety of normative systems: we examine the requirements for agents to have autonomy over their choice of norms, the degree of observability required in the system, and the norm enforcement methods. The paper concludes with an discussion of open issues.

Transient and sustained elementary flux mode networks on a catalytic string-based chemical evolution model

Theoretical models designed to test the metabolism-first hypothesis for prebiotic evolution have yield strong indications about the hypothesis validity but could sometimes use a more extensive identification between model objects and real objects towards a more meaningful interpretation of results. In an attempt to go in that direction, the string-based model SSE ("steady state evolution") was developed, where abstract molecules (strings) and catalytic interaction rules are based on some of the most important features of carbon compounds in biological chemistry. The system is open with a random inflow and outflow of strings but also with a permanent string food source. Although specific catalysis is a key aspect of the model, used to define reaction rules, the focus is on energetics rather than kinetics. Standard energy change tables were constructed and used with standard formation reactions to track energy flows through the interpretation of equilibrium constant values. Detection of metabolic networks on the reaction system was done with elementary flux mode (EFM) analysis. The combination of these model design and analysis options enabled obtaining metabolic and catalytic networks showing several central features of biological metabolism, some more clearly than in previous models: metabolic networks with stepwise synthesis, energy coupling, catalysts regulation, SN2 coupling, redox coupling, intermediate cycling, coupled inverse pathways (metabolic cycling), autocatalytic cycles and catalytic cascades. The results strongly suggest that the main biological metabolism features, including the genotype-phenotype interpretation, are caused by the principles of catalytic systems and are prior to modern genetic systems principles. It also gives further theoretical support to the thesis that the basic features of biologic metabolism are a consequence of the time evolution of a random catalyst search working on an open system with a permanent food source. The importance of the food source characteristics and evolutionary possibilities are discussed.

Scientific perspectivism: A philosopher of science's response to the challenge of big data biology

Big data biology-bioinformatics, computational biology, systems biology (including 'omics'), and synthetic biology-raises a number of issues for the philosophy of science. This article deals with several such: Is data-intensive biology a new kind of science, presumably post-reductionistic? To what extent is big data biology data-driven? Can data 'speak for themselves?' I discuss these issues by way of a reflection on Carl Woese's worry that "a society that permits biology to become an engineering discipline, that allows that science to slip into the role of changing the living world without trying to understand it, is a danger to itself." And I argue that scientific perspectivism, a philosophical stance represented prominently by Giere, Van Fraassen, and Wimsatt, according to which science cannot as a matter of principle transcend our human perspective, provides the best resources currently at our disposal to tackle many of the philosophical issues implied in the modeling of complex, multilevel/multiscale phenomena.

Modeling the C. elegans nematode and its environment using a particle system

A particle system, as understood in computer science, is a novel technique for modeling living organisms in their environment. Such particle systems have traditionally been used for modeling the complex dynamics of fluids and gases. In the present study, a particle system was devised to model the movement and feeding behavior of the nematode Caenorhabditis elegans in three different virtual environments: gel, liquid, and soil. The results demonstrate that distinct movements of the nematode can be attributed to its mechanical interactions with the virtual environment. These results also revealed emergent properties associated with modeling organisms within environment-based systems.

An artificial ecosystem: emergent dynamics and lifelike properties

We discuss modeling and analysis of an artificial ecosystem. The ecosystem consists of basic elements, scents, plants, and animals. There are two species of animals: worms and beetles. As beetles absorb energy from worms, which absorb energy from blades of grass, which absorb energy from water, there is a food chain connecting animals to basic elements. The novelty of our approach lies in the modeling technique: we model the entire ecosystem using a single particle system. Consequently, the physical interaction dynamics not only shows emergent dynamics, but also some interesting lifelike properties. As the main contribution, we formalize the particle system and use it to model and analyze the ecosystem. We consider here several scenarios with nontrivial interaction dynamics.