Alexandra S. Penn

The concurrent evolution of cooperation and the population structures that support it.

The evolution of cooperation often depends upon population structure, yet nearly all models of cooperation implicitly assume that this structure remains static. This is a simplifying assumption, because most organisms possess genetic traits that affect their population structure to some degree. These traits, such as a group size preference, affect the relatedness of interacting individuals and hence the opportunity for kin or group selection. We argue that models that do not explicitly consider their evolution cannot provide a satisfactory account of the origin of cooperation, because they cannot explain how the prerequisite population structures arise. Here, we consider the concurrent evolution of genetic traits that affect population structure, with those that affect social behavior. We show that not only does population structure drive social evolution, as in previous models, but that the opportunity for cooperation can in turn drive the creation of population structures that support it. This occurs through the generation of linkage disequilibrium between socio‐behavioral and population‐structuring traits, such that direct kin selection on social behavior creates indirect selection pressure on population structure. We illustrate our argument with a model of the concurrent evolution of group size preference and social behavior.

Linear and sigmoidal fuzzy cognitive maps: An analysis of fixed points

Fuzzy cognitive mapping is commonly used as a participatory modelling technique whereby stakeholders create a semi-quantitative model of a system of interest. This model is often turned into an iterative map, which should (ideally) have a unique stable fixed point. Several methods of doing this have been used in the literature but little attention has been paid to differences in output such different approaches produce, or whether there is indeed a unique stable fixed point. In this paper, we seek to highlight and address some of these issues. In particular we state conditions under which the ordering of the variables at stable fixed points of the linear fuzzy cognitive map (iterated to) is unique. Also, we state a condition (and an explicit bound on a parameter) under which a sigmoidal fuzzy cognitive map is guaranteed to have a unique fixed point, which is stable. These generic results suggest ways to refine the methodology of fuzzy cognitive mapping. We highlight how they were used in an ongoing case study of the shift towards a bio-based economy in the Humber region of the UK.

Can selfish symbioses effect higher-level selection?

The role of symbiosis in macro-evolution is poorly understood. On the one hand, symbiosis seems to be a perfectly normal manifestation of individual selection, on the other hand, in some of the major transitions in evolution it seems to be implicated in the creation of new higher-level units of selection. Here we present a model of individual selection for symbiotic relationships where individuals can genetically specify traits which partially control which other species they associate with - i.e. they can evolve species-specific grouping. We find that when the genetic evolution of symbiotic relationships occurs slowly compared to ecological population dynamics, symbioses form which canalise the combinations of species that commonly occur at local ESSs into new units of selection. Thus even though symbioses will only evolve if they are beneficial to the individual, we find that the symbiotic groups that form are selectively significant and result in combinations of species that are more cooperative than would be possible under individual selection. These findings thus provide a systematic mechanism for creating significant higher-level selective units from individual selection, and support the notion of a significant and systematic role of symbiosis in macroevolution.

Participatory development and analysis of a fuzzy cognitive map of the establishment of a bio-based economy in the Humber region.

Fuzzy Cognitive Mapping (FCM) is a widely used participatory modelling methodology in which stakeholders collaboratively develop a ‘cognitive map’ (a weighted, directed graph), representing the perceived causal structure of their system. This can be directly transformed by a workshop facilitator into simple mathematical models to be interrogated by participants by the end of the session. Such simple models provide thinking tools which can be used for discussion and exploration of complex issues, as well as sense checking the implications of suggested causal links. They increase stakeholder motivation and understanding of whole systems approaches, but cannot be separated from an intersubjective participatory context. Standard FCM methodologies make simplifying assumptions, which may strongly influence results, presenting particular challenges and opportunities. We report on a participatory process, involving local companies and organisations, focussing on the development of a bio-based economy in the Humber region. The initial cognitive map generated consisted of factors considered key for the development of the regional bio-based economy and their directional, weighted, causal interconnections. A verification and scenario generation procedure, to check the structure of the map and suggest modifications, was carried out with a second session. Participants agreed on updates to the original map and described two alternate potential causal structures. In a novel analysis all map structures were tested using two standard methodologies usually used independently: linear and sigmoidal FCMs, demonstrating some significantly different results alongside some broad similarities. We suggest a development of FCM methodology involving a sensitivity analysis with different mappings and discuss the use of this technique in the context of our case study. Using the results and analysis of our process, we discuss the limitations and benefits of the FCM methodology in this case and in general. We conclude by proposing an extended FCM methodology, including multiple functional mappings within one participant-constructed graph.

Individual selection for cooperative group formation

It is well known that certain environmental conditions, such as a spatially structured population, can promote the evolution of cooperative traits. However, such conditions are usually assumed to be externally imposed. In this paper, we present a model that allows the conditions that promote or hinder cooperation to arise adaptively via individual selection. Consequently, instead of selection simply favouring cooperation under imposed environmental conditions, in our model selection also operates on the conditions themselves via a niche construction process. Results are presented that show that the conditions that favour cooperation can evolve, even though those that favour selfish behaviour are also available and are initially selected for.

Extending Participatory Fuzzy Cognitive Mapping with a Control Nodes Methodology: A Case Study of the Development of a Bio-based Economy in the Humber Region, UK

Fuzzy Cognitive Mapping (FCM) is a widely used participatory modeling methodology in which stakeholders collaboratively develop a cognitive map (a weighted, directed graph), representing the perceived causal structure of their system. FCM can be an extremely useful tool to enable stakeholders to collaboratively represent and consolidate their understanding of the structure of their system. Analysis of an FCM using tools from network theory enables the calculation of “control configurations” for the system; subsets of system factors which if controlled could be used to drive the system to any given state. We have developed a technique that allows us to calculate all possible, minimally-sized control configurations of a stakeholder-generated FCM within a workshop context. In order to evaluate our results in terms of real world “controllability,” stakeholders score all factors on the basis of their ability to influence them, allowing us to rank the configurations by their potential local controllability. This provides a starting point for discussions about effective policy, or other interventions from the specific perspective of regional actors and decision makers. We describe this methodology and report on a participatory process in which it was tested: the construction of an FCM focusing on the development of a bio-based economy in the Humber region (UK) by key stakeholders from local companies and organizations. Results and stakeholder responses are discussed in the context of our case study, but also, more generally, in the context of the use of participatory modeling for decision making in complex socio-ecological-economic systems.

A web-based tool for identifying strategic intervention points in complex systems

Steering a complex system towards a desired outcome is a challenging task. The lack of clarity on the system’s exact architecture and the often scarce scientific data upon which to base the op- erationalisation of the dynamic rules that underpin the interactions between participant entities are two contributing factors. We describe an analytical approach that builds on Fuzzy Cognitive Map- ping (FCM) to address the latter and represent the system as a complex network. We apply results from network controllability to address the former and determine minimal control configurations - subsets of factors, or system levers, which comprise points for strategic intervention in steering the system. We have implemented the combination of these techniques in an analytical tool that runs in the browser, and generates all minimal control configurations of a complex network. We demonstrate our approach by reporting on our experience of working alongside industrial, local-government, and NGO stakeholders in the Humber region, UK. Our results are applied to the decision-making process involved in the transition of the region to a bio-based economy.

A New Home for a Vital Conversation: Introducing the ALife Societal Impact Section and Going Back to Bio-Inspiration for the Internet

Welcome to the new societal impact section of Artificial Life, the home for a community-wide conversation on how ALife can contribute to our common future. We inhabit an era of multiple, global-scale societal challenges that concern understanding and managing complex adaptive systems and living, lifelike, and hybrid technologies. Many of these challenges are genuinely existential. All are potentially profoundly disruptive for good or ill. The future of complexity, of adaptive, digital technologies, of synthetic biology and ecology, of ubiquitous AI, and of social, economic, and political upheaval is here, and our institutions and social norms seem unprepared to deal with it. This future clearly contains enormous challenges, but also tremendous opportunities to shape a better society. We believe that the artificial life community has a key role to play in this future and in the debate surrounding it, both as producers of potentially disruptive technologies and as a community with a deep-rooted experience in considering complex systems, as well as in connection with the philosophical approaches and tools required for the job. The boundary-spanning, open, and creative nature of the artificial life community makes us an ideal fit to engage in this vital debate. However, we must reflect critically on how our work can be genuinely, practically useful in the real world today as well as being fantastical and exploratory. We also have a great deal to learn from other disciplines and practices: the social sciences, humanities, and arts and those who deal day to day with societal problems on the ground. The aim of this section is to bring in voices from all over the artificial life community, so as to provide a home for an ongoing conversation about the current and potential societal impact

Steering a Complex Adaptive System: A Complexity Science Design Methodology Applied to an Industrial Ecosystem in the Humber Region, UK

Many important challenges facing society today involve the management of interlinked complex adaptive systems (CAS): coupled socio-economic and ecological systems composed of many interacting elements which have been created or partially created by human actions. As we explicitly wish to manage and transform these systems, engineering and design approaches have much to offer us, however they must be fundamentally modified to deal with CAS. These systems are not static artifacts, but dynamic, evolving and reflexive processes the behaviour of which is not straightforwardly predicable and which may respond in unexpected ways in response to our interventions. Additionally many of the complex systems which we would most like to influence have significant social components. Objective choices about design goals cannot be made and the integration of participatory or political processes may be required. In order to manage complex adaptive systems, we suggest a “steering” approach; an action or series of actions applied to a complex system and/or its environment for achieving a specific purpose. Steering combines tools from complexity science with whole systems design philosophy and is a continuous process which involves interacting with, monitoring and learning from the system in question. The techniques required for effective steering fall into two categories. Firstly we wish to understand, and indeed exploit, the systems’ structure and dynamics in order to intervene efficaciously with them. Hence we need techniques to uncover this structure and to choose points of intervention: system “levers” through which the system as a whole could be manipulated with system interventions designed accordingly. Secondly we frame those techniques within a participatory “adaptive management” structure (Waltner-Toews and Kay, 2005), which explicitly takes into account the adaptive nature of these systems and our limited capacity to fully model real world complex systems, by building in monitoring and feedback processes with which to modify our interventions as systems respond. We are currently applying this process to a case study aiming to facilitate regional decision making in an industrial ecosystem in the Humber region, UK. The region represents a significant proportion of UK infrastructure, energy generation capacity and CO2 production. However, there is a strong desire to develop the “Humber Gateway” as a renewable energy hub using the extensive agricultural hinterland and offshore and port facilities to support bio-based energy production and offshore wind and tidal generation. We have undertaken participatory modelling exercises in which stakeholders collaboratively constructed simple systems models of the development of their regional bio-based economy, the key factors of influence, drivers and their perceived interdependencies (Penn et al., 2013). Building on this approach we used a “control nodes” methodology from network theory (Liu et al., 2011) to determine the specific subsets of these factors which could theoretically be used to drive the system into any given state. This technique is combined with an evaluation of the practical controllability of each factor from the perspectives of the actors present to allow its use in real world contexts in which policy makers and industrial stakeholders must make decisions (Penn et al., In press.). Applying this hybrid approach in the real world context of the Humber allowed stakeholders to uncover and discuss possible novel points of intervention in their regional system, based both on its structure and their own differing abilities to influence different factors within it. This proved to be a useful starting point for debate on policy ideas and the importance of considering not just where to intervene, but who is able to intervene with a given factor. However this approach, like all network-based methods, is limited by its sensitivity to the network structure described. Additionally by the fact that once suitable control configurations are discovered, the method as yet gives us no indication of how to construct the time variable control inputs required to steer the system to a given state. Instead we must use such modelling as a “thinking tool” to provide principled starting points, to be combined with stakeholder expertise, for the participatory design of complex social systems.

Simpson's Paradox, Co-operation and Individuality in Bacterial Biofilms

Bacteria often live in group structures known as biofilms within which they commonly display co-operative behaviours, such as the production of public goods (Ghannoum & O’Toole 2004, Crespi 2001, West et al. 2007). Noncooperative cheats arise commonly in biofilms (de Vos et al. 2001, Schaber et al. 2004), but despite what theory might predict, (Hardin 1968, Rankin et al. 2007), co-operation does not seem to be disrupted. The stability of these behaviours requires explanation and could cast light on the evolution of multi-cellularity experimentally (e.g. Rainey & Rainey 2003, Griffin et al. 2004, Kreft 2004, Buckling et al. 2007). Theory tells us that repeated aggregation into local groups, interleaved with dispersal and remixing, can increase the level of cooperation in a population despite a selective disadvantage to cooperating within any group (Wilson 1980). This increase in global proportion of co-operators despite a decrease in all local proportions, caused by the differential growth of groups, is known as Simpson’s paradox (Simpson 1951). Given the microcolony (small sub-group) formation and dispersal behaviour observed in natural biofilms, it has been suggested that Simpson’s paradox may explain bacterial cooperation; but although it has been demonstrated in artificially constructed groups, it has not yet been demonstrated in a natural population (Chuang et al. 2009). Using the production of siderophores in Pseudomonas aeruginosa as a model system for co-operation (Varma & Chincholker 2007), we measured the change frequency of co-operator and siderophore-deficient cheat strains in-situ within microcolony structures over time. We detected significant within-type negative densitydependent effects which vary over microcolony development. The growth of types was self-limiting at different times: Cheat growth was negatively correlated with the proportion cheats during early stages of microcolony development, with wildtype growth negatively correlated with wild-type biomass later. However, we found no evidence of Simpson’s paradox (Penn et al. 2012). Instead we saw clear within-microcolony spatial structure (cheats occupying the interior portions of microcolonies) that may violate the assumption required for Simpson’s paradox that group members share equally in the public good. In fact, it seems that the extent of the group over which the public good is being shared is a dynamic entity. This group, which will be defined by a lower threshold siderophore concentration for effective iron chelation, codevelops with the biofilm as the result of an interaction between population dynamics and the react-diffusion processes within it. This has interesting consequences for understanding co-operation within biofilms as well as major transitions, as the group may potentially be influenced by the bacteria themselves in order to change the context of selection and promote within-microcolony “individuality”. I will discuss our observations and continuing work, both experimental and in simulation, in the broader context of a theoretical framework that suggests how factors which affect population structure, higher-level individuality and cooperative behaviour may co-evolve.