What are the contexts that give rise to cooperation as opposed to conflict? When should we love thy neighbor, turn the other cheek, or escalate? Game theory is an effort to formalize this problem such that we can ask what decisions we should make when the consequences of those decisions depend on the decisions of others. By this accounting, war is a game, as is negotiation, rock-paper-scissors, and love making. The difference is in the payoffs. Being rational is about making decisions that lead to the best outcomes. The hawks and doves of political foreign policy – who advocate for more or less aggressive military intervention – are rational beings in this world, because the nature of the payoffs demands certain kinds of responses. The pragmatics of high stakes games underpin quotes like John F. Kennedy’s "We can secure peace only by preparing for war." But is Kennedy’s statement rational by the logic of the game in which it is embedded? Is it rational when games of conflict are played repeatedly in a network of global interactions? By combining game theory with network science, we can make some progress toward understanding these issues.

Animal behavior is subject to the action of natural selection, favoring individuals that behave in ways that maximize their fitness by promoting individual survival and reproductive success. Cultural evolution plays an important role, with behavioral traits of surprising complexity spreading rapidly through a population. Behavioral ecologists measure the costs and benefits of alternative types of behavior to gain an understanding of basic behavioral processes such as territory defense, foraging, and mating. This cost–benefit approach allows quantitative predictions of behavior tightly tied to fitness, such as how long to guard a mate, or how long to forage at a particular location before moving on. Both physiological factors, such as the need to keep eggs warm, and ecological factors, such as the spatial distribution of resources, can influence the evolution of mating systems. In many species, individuals cooperate with each other in procuring food or defending against predators. Hamilton’s model of indirect selection is one possible explanation for the evolution of behavior favoring relatives, including the astounding degree of cooperation in eusocial animals. But in some species, cooperation is common even among unrelated individuals; in these cases game theory models may help explain the evolution of cooperation.

In this chapter, we depart from the observation that a strong motivation for large firms to invest substantial amounts into R&D for an AGI is due to the winner-takes-all effects it may bestow on them. This feature, while important to incentivize AI investment, has the downside that it implies that AI arms races may take place. And the danger of an AI arms race is that it may result in an inferior AGI from a human safety perspective. In this chapter, we model such an AI arms race as an innovation contest and show how a government can steer such an arms race so as to obtain a better outcome in terms of the quality of the AGI. A crucial insight from our modeling is that the intention (or goals) of teams competing in an AGI race, as well as the possibility of an intermediate outcome (“second prize”), may be important.

This chapter deals with how public policy can steer AI, by taking how it can impact the use of big data, one of the key inputs required for AI. Essentially, public policy can steer AI through putting conditions and limitations on data. But data itself can help improve public policy – also in the area of economic policymaking. Hence, this chapter touches on the future potential of economic policy improvements through AI. More specifically, we discuss under what conditions the availability of large data sets can support and enhance public policy effectiveness – including in the use of AI – along two main directions. We analyze how big data can help existing policy measures to improve their effectiveness and, second, we discuss how the availability of big data can suggest new, not yet implemented, policy solutions that can improve upon existing ones. The key message of this chapter is that the desirability of big data and AI to enhance policymaking depends on the goal of public authorities, and on aspects such as the cost of data collection and storage and the complexity and importance of the policy issue.

Linguistic pragmatics is one of the fastest growing fields in contemporary theoretical linguistics. It grew from the influential work of philosophers such as Grice, Austin, Stalnaker, Lewis, and others on context and communicative inferences. It engages directly with general rationality, theory of mind, and systems of intention. One of the major debates in pragmatics has been where to precisely draw the line between semantic phenomena and pragmatic phenomena. In this chapter, three classical and influential ideas on the nature of pragmatics, courtesy of Grice (1975), Stalnaker (1978), and Lewis (1979), are discussed. This discussion leads to three further general philosophical frameworks for separating semantic from pragmatic processes and analyses that have roots in the aforementioned triumvirate: (P1) the indexical conception, (P2) the cognitivist conception, and (P3) social-inferential conception. Each option offers a different demarcation. Finally, three linguistic theories of pragmatics are selected as candidate representations of the contemporary state of the art: (L1) optimality-theoretic pragmatics, (L2) game-theoretic pragmatics, and (L3) Bayesian pragmatics. It’s shown that each of these prominent frameworks exploit the philosophical demarcations (P1–P3) presented to different degrees.

The chapter discusses the field of cultural evolution, which emerged in the wake of sociobiology and alongside evolutionary psychology and human behavioral ecology in the 1970s. Cultural evolution theory can be divided into three parts: investigating the genetic evolution of psychological capacities underlying culture, studying how populations adapt to their environments, and exploring how genes and culture influence each other’s evolutionary dynamics. The first sections of the chapter examine how human culture differs from nonhuman culture, why and how culture evolved in humans during the Pleistocene, how our cultural capacity predisposed us to be cooperative, and how cultural group selection created the conditions for the genetic evolution of prosocial preferences. Addressing a common criticism that cultural evolution focuses too heavily on mathematical modeling, subsequent sections showcase the recent explosion of empirical research that has been inspired by cultural evolutionary theory. This research is divided into three methods: fieldwork, experiments, and phylogenetics. The chapter concludes by noting that the dividing lines between cultural evolution and human behavioral ecology are blurring, with students of each subfield increasingly borrowing from the other, suggesting that these labels may soon be obsolete.

A core contention woven into the fabric of Sun Tzu’s thinking is that all situations faced by a strategic actor, even those that appear on their face to be losing ones, hold seeds of opportunity that, if grasped correctly, can be parlayed into strategic advantage.1 An illustrative statement starts off Passage #5.1 below.

There are two Sun Tzu verses which, by Sun Tzu’s own affirmations, may be seen as summations of the active ingredient of his way of war. One is Theme #6’s centerpiece verse III.4 (Passage #6.1).

Constitutions - in a political sense - provide solutions to the age old problem of leadership changes and how majorities and minorities should interact. If political communities solve these problems the can better coordinate their efforts, which in turn will give them a competitive advantage (military, fiscal, economic, etc.) to other (less coordinated) political communities. This chapter looks into the political effects constitutions have, and how they try to calibrate these kind of balances. It also look into the possibilities of calculating or engineering (new) balances like this, for instance for divided societies and transitional democracies (constitutional engineering)

A formal, decision-theoretic model of relationality is presented. Some elementary (game-theoretic) examples are provided that show how relationality can transform a social network into a collaborative community of other-regarding individuals. Early results from experimental economic games are provided that illustrate the models explanatory power. The model is compared, side-by-side, with Ostroms conceptualization of commons management as a repeated game with social sanctions.