We present a new modelling approach for complex systems incorporating a dynamic environment and individuals with agency. We do this through multiple models at different levels. We develop a common meta-model for these kinds of models. The meta-model captures the concepts of agents moving and interacting on a dynamic network, to provide the power of an agent based model situated in the context of a dynamic and changing environment. The addition of context allows us to isolate the decision process of the agent from the constraints and resources provided by the environment, so we can consider separately the effect of changes in the environment from changes in the agents’ decision process, and changes caused by agents acting differently based on their learning from, and adapting to, the changed environment. We develop a generalised platform model for implementing different complex systems conforming to the meta-model. We illustrate the approach by developing a domain model for a particular system of interest, a simplified model of declining mobility, which we use to guide the specialisation of the generic platform model to an implementation and to perform simulation experiments.

Extreme ideas and opinions are commonly seen growing in many aspects of today's society, ranging from political ideology to healthcare choices and from dietary preferences to technological innovation. Such a trend may be understood as an outcome of a spontaneous dynamical process driven by the recent advancement of information communication technology that allows people to preferentially select their information sources. Here we study, using an agent-based model of adaptive social network dynamics, how extreme ideas may arise in society in which individuals simply try to conform to social norm within their social neighborhood. Our model assumes that each node gradually assimilates its state to local social norm, i.e., the average of its in-neighbors’ states, while also changing edge weights based on their states. Numerical simulations revealed that, when individuals tend to practice homophily by strengthening their ties selectively to neighbors with similar states, there tends to be many extreme ideas emerging in society while the network topology tends to become fragmented. Such outcomes are mitigated, however, when individuals also practice novelty-seeking behavior by increasing attention to neighbors whose ideas do not conform to the local social norm. These results paint a paradoxical picture of complex social processes — society produces difference when individuals seek sameness, or society reaches sameness when individuals seek difference.

Reducing the peak energy consumption of households is essential for the effective use of renewable energy sources, in order to ensure that as much household demand as possible can be met by renewable sources. This entails spreading out the use of high-powered appliances such as dishwashers and washing machines throughout the day. Traditional approaches to this problem have relied on differential pricing set by a centralised utility company. But this mechanism has not been effective in promoting widespread shifting of appliance usage. Here we consider an alternative decentralised mechanism, where agents receive an initial allocation of timeslots to use their appliances and can then exchange these with other agents. If agents are willing to be more flexible in the exchanges they accept, then overall satisfaction, in terms of the percentage of agents’ time-slot preferences that are satisfied, will increase. This requires a mechanism that can incentivise agents to be more flexible. Building on previous work, we show that a mechanism incorporating social capital — the tracking of favours given and received — can incentivise agents to act flexibly and give favours by accepting exchanges that do not immediately benefit them. We demonstrate that a mechanism that tracks favours increases the overall satisfaction of agents, and crucially allows social agents that give favours to outcompete selfish agents that do not under payoff-biased social learning. Thus, even completely self-interested agents are expected to learn to produce socially beneficial outcomes.

The latest report from the Intergovernmental Panel on Climate Change (IPCC) estimated that humanity has a time window of about 12 years in order to prevent anthropogenic climate change of catastrophic magnitude. Green house gas emission from air travel, which is currently rising, is possibly one of the factors that can be most readily reduced. Within this context, we advocate for the re-design of academic conferences in order to decrease their environmental footprint. Today, virtual technologies hold the promise to substitute many forms of physical interactions and increasingly make their way into conferences to reduce the number of travelling delegates. Here, we present the results of a survey in which we gathered the opinion on this topic of academics worldwide. Results suggest there is ample room for challenging the (dangerous) business-as-usual inertia of scientific lifestyle.

Cities are pervasive and they are some of the most powerful ecosystems of the Anthropocene. They have been around for almost 10000 years, and by the year 2050 most humans will be living in a city. Although it is known that cities have an impact to different scales, from the very local to the whole Earth System, its implications are far from being understood. Studying cities as organismic systems has been a productive strategy and favourable to complex adaptive systems analyses. However, the organismic view is to a great extent metaphorical, focusing exclusively on human activity, instead I argue for an approach that actually considers life processes as constitutive to them. In this extended abstract I suggest a conceptual framing for a synthetic approach to cities in which life processes are paramount for their understanding. Specifically, I will focus on two aspects: 1) the human-teleological component of cities and 2) the role of life processes organisationally closing the city, and bringing forth a self-generated unity and identity and the conditions for its own evolution. I believe that due to the increasing interest of the ALife community in tackling social issues, ALife unique insights and methods can be of great value in understanding cities and dealing with the social-ecological challenges they pose. A definition of cities from a synthetic perspective can help the ALife community to put into action its epistemic arsenal.

One of the research questions in ALife that could contribute greatly to social sustainability issues is how dynamic metastates of a complex system may be sustained through continual adaptive changes, or suppleness (Bedau, 1998). The idea of sustainability by suppleness is fundamentally different from conventional ideas of sustainability by robustness or resilience, and it is directly linked to open-endedness , a topic that has recently attracted significant attention in the ALife community (Taylor et al., 2016). Understanding and implementing mechanisms of suppleness and open-endedness may provide novel perspectives of many of today’s socio-economic, socio-ecological and socio-technological problems that call for new strategies to cope with inevitable environmental/contextual changes. This short essay provides a non-exhaustive list of research questions on this topic and encourages ALife researchers to play a leading role in this interdisciplinary collaboration endeavor.

By Artificial Democratic Life we mean the design and deployment of artificial (digital) infrastructures aimed at enhancing or improving social democratic life. Artificial Life, as a discipline and as a community, has much to contribute to the contemporary challenge of redesigning democracy in the network era, in understanding and designing democracy as a form of life: one that evolves into increasingly higher complexity and diversity while preserving homeostatic invariants and designing the infrastructures capable to resiliently enhance it. We identify some opportunities and specific challenges that can be faced using Alife simulation techniques and conceptual resources.

Taking inspiration from nature for meta-heuristics has proven popular and relatively successful. Many are inspired by the collective intelligence exhibited by insects, fish and birds. However, there is a question over their scalability to the types of complex problems experienced in the modern world. Natural systems evolved to solve simpler problems effectively, replicating these processes for complex problems may suffer from inefficiencies. Several causal factors can impact scalability; computational complexity, memory requirements or pure problem intractability. Supporting evidence is provided using a case study in Ant Colony Optimisation (ACO) regards tackling increasingly complex real-world fleet optimisation problems. This paper hypothesizes that contrary to common intuition, bio-inspired collective intelligence techniques by their very nature exhibit poor scalability in cases of high dimensionality when large degrees of decision making are required. Facilitating scaling of bio-inspired algorithms necessitates reducing this decision making. To support this hypothesis, an enhanced Partial-ACO technique is presented which effectively reduces ant decision making. Reducing the decision making required by ants by up to 90% results in markedly improved effectiveness and reduced runtimes for increasingly complex fleet optimisation problems. Reductions in traversal timings of 40–50% are achieved for problems with up to 45 vehicles and 437 jobs.