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This volume examines the theoretical and empirical landscape of social entrepreneurship in both non-profit and profit sectors. It extends the traditional view of social entrepreneurship to include the environmental and institutional factors that affect the emergence of social entrepreneurship activities, such as formal laws, regulations, procedures and informal institutions. The editors aim to provide evidence and increased understanding of this growing phenomenon. Social Entrepreneurship is gaining recognition as a key element of economic and social development. It embraces a wide set of situations with a broad scope of activities in for-profit and non-profit organizations interested in social performance and/or in economically profitable performance, with an emphasis on achieving social aim. In the strict sense, social entrepreneurship corresponds to entrepreneurs whose main concern is to achieve social objectives rather than to obtain personal financial profits. However, there is still much to be learned about the dynamics and processes of social entrepreneurship. The current literature in the field has tended to focus on psychological experiences and personal characteristics, or on organizational perspectives such as resources, capabilities and leadership. This book intends to provide theoretical frameworks and empirical studies to this very new and broad field. Specifically, this book provides a collection of contemporary research in the following topics: How to create opportunity through social innovation How to detect entrepreneurial opportunity to meet social needs How to develop social entrepreneurship, while still seeking profits How to discover opportunities for different forms of social entrepreneurship Featuring contributions from around the world, this book is a valuable source for students, academics, researchers, policy makers, and professionals in the area of social entrepreneurship.

In a binary decision game in which players strategically help certain individuals but not others, simple moral principles maximize cooperation, even when including the historical reputations of players. The complexity of cooperation Of all the schemes invoked in the quest to discover how altruism evolved in a world that's red in tooth and claw, indirect reciprocity is perhaps the most complex. It means that an agent can punish a defector and incur a cost, in the expectation of being rewarded later by a third party. Moral choices are required, and the response of the third party depends on the reputation of the agent and the defector. The various options are so complex that some believe the human mind incapable of computing them in a reasonable time frame, and yet people make such choices easily. How can this be? Here the authors simulate choices faced by third parties in situations of indirect reciprocity and find that the choices that lead to the greatest cooperation needn't be the most complex. 'Stern judging', whereby 'all that matters is what you do and the reputation of your opponent when you act: help good people and refuse help otherwise, and we shall be nice to you; otherwise, you will be punished', is of merely second-order complexity, yet even pre-verbal infants use it. Indirect reciprocity is the most elaborate and cognitively demanding 1 of all known cooperation mechanisms 2 , and is the most specifically human 1 , 3 because it involves reputation and status. By helping someone, individuals may increase their reputation, which may change the predisposition of others to help them in future. The revision of an individual’s reputation depends on the social norms that establish what characterizes a good or bad action and thus provide a basis for morality 3 . Norms based on indirect reciprocity are often sufficiently complex that an individual’s ability to follow subjective rules becomes important 4 , 5 , 6 , even in models that disregard the past reputations of individuals, and reduce reputations to either ‘good’ or ‘bad’ and actions to binary decisions 7 , 8 . Here we include past reputations in such a model and identify the key pattern in the associated norms that promotes cooperation. Of the norms that comply with this pattern, the one that leads to maximal cooperation (greater than 90 per cent) with minimum complexity does not discriminate on the basis of past reputation; the relative performance of this norm is particularly evident when we consider a ‘complexity cost’ in the decision process. This combination of high cooperation and low complexity suggests that simple moral principles can elicit cooperation even in complex environments.

Indirect reciprocity (IR) is a key mechanism to understand cooperation among unrelated individuals. It involves reputations and complex information processing, arising from social interactions. By helping someone, individuals may improve their reputation, which may be shared in a population and change the predisposition of others to reciprocate in the future. The reputation of individuals depends, in turn, on social norms that define a good or bad action, offering a computational and mathematical appealing way of studying the evolution of moral systems. Over the years, theoretical and empirical research has unveiled many features of cooperation under IR, exploring norms with varying degrees of complexity and information requirements. Recent results suggest that costly reputation spread, interaction observability and empathy are determinants of cooperation under IR. Importantly, such characteristics probably impact the level of complexity and information requirements for IR to sustain cooperation. In this review, we present and discuss those recent results. We provide a synthesis of theoretical models and discuss previous conclusions through the lens of evolutionary game theory and cognitive complexity. We highlight open questions and suggest future research in this domain. This article is part of the theme issue 'The language of cooperation: reputation and honest signalling'.

Indirect reciprocity, besides providing a convenient framework to address the evolution of moral systems, offers a simple and plausible explanation for the prevalence of cooperation among unrelated individuals. By helping someone, an individual may increase her/his reputation, which may change the pre-disposition of others to help her/him in the future. This, however, depends on what is reckoned as a good or a bad action, i.e., on the adopted social norm responsible for raising or damaging a reputation. In particular, it remains an open question which social norms are able to foster cooperation in small-scale societies, while enduring the wide plethora of stochastic affects inherent to finite populations. Here we address this problem by studying the stochastic dynamics of cooperation under distinct social norms, showing that the leading norms capable of promoting cooperation depend on the community size. However, only a single norm systematically leads to the highest cooperative standards in small communities. That simple norm dictates that only whoever cooperates with good individuals, and defects against bad ones, deserves a good reputation, a pattern that proves robust to errors, mutations and variations in the intensity of selection.

Human cooperation: Good works Although humans often cooperate with each other, the temptation to forego the public good mostly wins over collective cooperative action, leading to the so-called 'tragedy of the commons'. Many existing models treat individuals as equivalent, ignoring diversity and population structure. Santos et al . show theoretically that social diversity, introduced via heterogeneous graphs, promotes the emergence of cooperation in public goods games. Humans often cooperate with each other, but the temptation to forgo the public good mostly wins over collective cooperative action. Many existing models treat individuals as equivalent, ignoring diversity and population structure; however, here it's shown theoretically that social diversity, introduced via heterogeneous graphs, promotes the emergence of cooperation in public goods games. Humans often cooperate in public goods games 1 , 2 , 3 and situations ranging from family issues to global warming 4 , 5 . However, evolutionary game theory predicts 4 , 6 that the temptation to forgo the public good mostly wins over collective cooperative action, and this is often also seen in economic experiments 7 . Here we show how social diversity provides an escape from this apparent paradox. Up to now, individuals have been treated as equivalent in all respects 4 , 8 , in sharp contrast with real-life situations, where diversity is ubiquitous. We introduce social diversity by means of heterogeneous graphs and show that cooperation is promoted by the diversity associated with the number and size of the public goods game in which each individual participates and with the individual contribution to each such game. When social ties follow a scale-free distribution 9 , cooperation is enhanced whenever all individuals are expected to contribute a fixed amount irrespective of the plethora of public goods games in which they engage. Our results may help to explain the emergence of cooperation in the absence of mechanisms based on individual reputation and punishment 10 , 11 , 12 . Combining social diversity with reputation and punishment will provide instrumental clues on the self-organization of social communities and their economical implications.

The field of Artificial Intelligence (AI) is going through a period of great expectations, introducing a certain level of anxiety in research, business and also policy. This anxiety is further energised by an AI race narrative that makes people believe they might be missing out. Whether real or not, a belief in this narrative may be detrimental as some stake-holders will feel obliged to cut corners on safety precautions, or ignore societal consequences just to “win”. Starting from a baseline model that describes a broad class of technology races where winners draw a significant benefit compared to others (such as AI advances, patent race, pharmaceutical technologies), we investigate here how positive (rewards) and negative (punishments) incentives may beneficially influence the outcomes. We uncover conditions in which punishment is either capable of reducing the development speed of unsafe participants or has the capacity to reduce innovation through over-regulation. Alternatively, we show that, in several scenarios, rewarding those that follow safety measures may increase the development speed while ensuring safe choices. Moreover, in the latter regimes, rewards do not suffer from the issue of over-regulation as is the case for punishment. Overall, our findings provide valuable insights into the nature and kinds of regulatory actions most suitable to improve safety compliance in the contexts of both smooth and sudden technological shifts.

Mitigating climate change effects involves strategic decisions by individuals that may choose to limit their emissions at a cost. Everyone shares the ensuing benefits and thereby individuals can free ride on the effort of others, which may lead to the tragedy of the commons. For this reason, climate action can be conveniently formulated in terms of Public Goods Dilemmas often assuming that a minimum collective effort is required to ensure any benefit, and that decision-making may be contingent on the risk associated with future losses. Here we investigate the impact of reward and punishment in this type of collective endeavors - coined as collective-risk dilemmas - by means of a dynamic, evolutionary approach. We show that rewards (positive incentives) are essential to initiate cooperation, mostly when the perception of risk is low. On the other hand, we find that sanctions (negative incentives) are instrumental to maintain cooperation. Altogether, our results are gratifying, given the a-priori limitations of effectively implementing sanctions in international agreements. Finally, we show that whenever collective action is most challenging to succeed, the best results are obtained when both rewards and sanctions are synergistically combined into a single policy.

From group hunting to global warming, how to deal with collective action may be formulated in terms of a public goods game of cooperation. In most cases, contributions depend on the risk of future losses. Here, we introduce an evolutionary dynamics approach to a broad class of cooperation problems in which attempting to minimize future losses turns the risk of failure into a central issue in individual decisions. We find that decisions within small groups under high risk and stringent requirements to success significantly raise the chances of coordinating actions and escaping the tragedy of the commons. We also offer insights on the scale at which public goods problems of cooperation are best solved. Instead of large-scale endeavors involving most of the population, which as we argue, may be counterproductive to achieve cooperation, the joint combination of local agreements within groups that are small compared with the population at risk is prone to significantly raise the probability of success. In addition, our model predicts that, if one takes into consideration that groups of different sizes are interwoven in complex networks of contacts, the chances for global coordination in an overall cooperating state are further enhanced.

It is widely known how the human ability to cooperate has influenced the thriving of our species. However, as we move towards a hybrid human-machine future, it is still unclear how the introduction of artificial agents in our social interactions affect this cooperative capacity. In a one-shot collective risk dilemma, where enough members of a group must cooperate in order to avoid a collective disaster, we study the evolutionary dynamics of cooperation in a hybrid population. In our model, we consider a hybrid population composed of both adaptive and fixed behavior agents. The latter serve as proxies for the machine-like behavior of artificially intelligent agents who implement stochastic strategies previously learned offline. We observe that the adaptive individuals adjust their behavior in function of the presence of artificial agents in their groups to compensate their cooperative (or lack of thereof) efforts. We also find that risk plays a determinant role when assessing whether or not we should form hybrid teams to tackle a collective risk dilemma. When the risk of collective disaster is high, cooperation in the adaptive population falls dramatically in the presence of cooperative artificial agents. A story of compensation, rather than cooperation, where adaptive agents have to secure group success when the artificial agents are not cooperative enough, but will rather not cooperate if the others do so. On the contrary, when risk of collective disaster is low, success is highly improved while cooperation levels within the adaptive population remain the same. Artificial agents can improve the collective success of hybrid teams. However, their application requires a true risk assessment of the situation in order to actually benefit the adaptive population (i.e. the humans) in the long-term.

Stroke-related disruptions in functional connectivity (FC) often spread beyond lesioned areas and, given the localized nature of lesions, it is unclear how the recovery of FC is orchestrated on a global scale. Since recovery is accompanied by long-term changes in excitability, we propose excitatory-inhibitory (E-I) homeostasis as a driving mechanism. We present a large-scale model of the neocortex, with synaptic scaling of local inhibition, showing how E-I homeostasis can drive the post-lesion restoration of FC and linking it to changes in excitability. We show that functional networks could reorganize to recover disrupted modularity and small-worldness, but not network dynamics, suggesting the need to consider forms of plasticity beyond synaptic scaling of inhibition. On average, we observed widespread increases in excitability, with the emergence of complex lesion-dependent patterns related to biomarkers of relevant side effects of stroke, such as epilepsy, depression and chronic pain. In summary, our results show that the effects of E-I homeostasis extend beyond local E-I balance, driving the restoration of global properties of FC, and relating to post-stroke symptomatology. Therefore, we suggest the framework of E-I homeostasis as a relevant theoretical foundation for the study of stroke recovery and for understanding the emergence of meaningful features of FC from local dynamics.