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The Instinct Planner is a new biologically inspired reactive planner, based on an established behaviour based robotics methodology and its reactive planner component—the POSH planner implementation. It includes several significant enhancements that facilitate plan design and runtime debugging. It has been specifically designed for low power processors and has a tiny memory footprint. Written in C++, it runs eciently on both Arduino(Atmel AVR) and Microsoft VC++ environments and has been deployed within a low cost maker robot to study AI Transparency. Plans may be authored using a variety of tools including a new visual design language, currently implemented using the Dia drawing package.
Artificial intelligence (AI) is a technical term often referring to artifacts used to detect contexts for human actions, or sometimes also for machines able to effect actions in response to detected contexts. Our capacity to build such artifacts has been increasing, and with it the impact they have on our society. This does not alter the fundamental roots or motivations of law, regulation, or diplomacy, which rest on persuading humans to behave in a way that provides sustainable security for humans. It does however alter nearly every other aspect of human social behaviour, including making accountability and responsibility potentially easier to trace. This chapter reviews the nature and implications of AI with particular attention to how they impinge on possible applications to and of law.
Artificial intelligence (AI) is a technical term referring to artifacts used to detect contexts or to effect actions in response to detected contexts. Our capacity to build such artifacts has been increasing, and with it the impact they have on our society. This article first documents the social and economic changes brought about by our use of AI, particularly but not exclusively focusing on the decade since the 2007 advent of smartphones, which contribute substantially to “big data” and therefore the efficacy of machine learning. It then projects from this political, economic, and personal challenges confronting humanity in the near future, including policy recommendations. Overall, AI is not as unusual a technology as expected, but this very lack of expected form may have exposed us to a significantly increased urgency concerning familiar challenges. In particular, the identity and autonomy of both individuals and nations is challenged by the increased accessibility of knowledge.
Cooperative behaviour is a fundamental strategy for survival; it positively affects economies, social relationships, and makes larger societal structures possible. People vary, however, in their willingness to engage in cooperative behaviour in a particular context.
Here we examine whether AI can be effectively used to to alter individuals' implicit understanding of cooperative dynamics, and hence increase cooperation and participation in public goods projects. We developed an intervention---the Sustainability Game (SG)---to allow players to experience the consequences of individual investment strategies on a sustainable society. %, when personal well being, communal space, and resources limitations are taken into consideration.
Results show that the intervention significantly increases individuals' cooperative behaviour in partially anonymised public goods contexts, but enhances competition one-on-one. This indicates our intervention does improve transparency of the systemic consequences of individual cooperative behaviour.
Communication
(2018)
From a traditional engineering perspective, communication is about effecting control over a distance, and its primary concern is the reliability of transmission. This chapter reviews communication in nature, describing its evolution from the perspective of the selfish gene. Communication in nature is ubiquitous and generally honest, and arises as much from collaboration as manipulation. We show that context and relevance allow effective communication with little information transfer, particularly between organisms with similar capacities and goals. Human language differs fundamentally from the non-verbal communication we share with other animals; robots may need to accommodate both. We document progress in AI capacities to generate synthetic emotion and to sense and classify human emotion. Communication in contemporary biomimetic systems is between robots in swarm robotics, but also between robot and human for both autonomous and collaborative systems. We suggest increased future emphasis on capacities to receive and comprehend signs, and on the pragmatic utility of communication and cooperation.
Gene regulatory networks underlie every aspect of life; better understanding their assembly would better our understanding of evolution more generally. For example, evolutionary theory typically assumed that low-fitness intermediary pathways are not a significant factor in evolution, yet there is substantial empirical evidence of compensatory mutation. Here we revise theoretical assumptions to explore the possibility that compensatory mutation may drive rapid evolutionary recovery. Using a well-established in silico model of gene regulatory networks, we show that assuming only that deleterious mutations are not fatal, compensatory mutation is surprisingly frequent. Further, we find that it entails biases that drive the evolution of regulatory pathways. In our simulations, we find compensatory mutation to be common during periods of relaxed selection, with 8-15% of degraded networks having regulatory function restored by a single randomly-generated additional mutation. Though this process reduces average robustness, proportionally higher robustness is found in networks where compensatory mutations occur close to the deleterious mutation site, or where the compensatory mutation results in a large regulatory effect size. This location- and size-specific robustness systematically biases which networks are purged by selection for network stability, producing emergent changes to the population of regulatory networks. We show that over time, large-effect and co-located mutations accumulate, assuming only that episodes of relaxed selection occur, even very rarely. This accumulation results in an increase in regulatory complexity. Our findings help explain a process by which large-effect mutations structure complex regulatory networks, and may account for the speed and pervasiveness of observed occurrence of compensatory mutation, for example in the context of antibiotic resistance, which we discuss. If sustained by in vitro experiments, these results promise a significant breakthrough in the understanding of evolutionary and regulatory processes.
Social and political polarization is a significant source of conflict and poor governance in many societies. Thus, understanding its causes has become a priority of scholars across many disciplines. Here we demonstrate that shifts in socialization strategies analogous to political polarization and identity politics can arise as a locally-beneficial response to both rising wealth inequality and economic decline. Adopting a perspective of cultural evolution, we develop a framework to study the emergence of polarization under shifting economic environments. In many contexts, interacting with diverse out-groups confers benefits from innovation and exploration greater than those that arise from interacting exclusively with a homogeneous in-group. However, when the economic environment favors risk-aversion, a strategy of seeking low-risk interactions can be important to maintaining individual solvency. To capture this dynamic, we assume that in-group interactions have a lower expected outcome, but a more certain one. Thus in-group interactions are less risky than out-group interactions. Our model shows that under conditions of economic decline or increasing wealth inequality, some members of the population benefit from adopting a risk-averse, in-group favoring strategy. Moreover, we show that such in-group polarization can spread rapidly to the whole population and persist even when the conditions that produced it have reversed. Finally we offer empirical support for the role of income inequality as a driver of affective polarization in the United States, mirroring findings on a panel of developed democracies. Our work provides a framework for studying how disparate forces interplay, via cultural evolution, to shape patterns of identity, and unifies what are often seen as conflicting explanations for political polarization: identity threat versus economic anxiety.
Regulation plays a central role in modern governance; yet, we have limited knowledge of how subjects of regulation—particularly, private actors—act in the face of potentially adverse regulatory decisions. Here, we document and examine a novel lobbying strategy in the context of competition regulation, a strategy that exploits the regulator’s finite administrative capacities. Companies with merger cases under scrutiny by the European Commission’s Directorate General for Competition appear to be employing a strategy of ‘spamming the regulator,’ through the strategic and cumulative submission of economic expert assessments. Procedural pressures may result in an undeservedly favourable assessment of the merger. Based on quantitative and qualitative analyses of an original dataset of all complex merger cases in the European Union 2005–2020, we present evidence of this new strategy and a possible learning process among private actors. We suggest remedies to ensure regulatory effectiveness in the face of this novel strategy.