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The world is witnessing an unprecedented disruption due to the COVID-19 pandemic in almost all spheres of socio-economic activity. This black swan moment is unprecedented since the advent of the Industrial Revolution. Simultaneously, there is a shift towards effective use of data and technology. There is an exponential increase in the quantum of data collected and has subsequently necessitated a paradigm shift in the functioning of Industry 4.0. Data intelligence, data science, artificial intelligence, machine learning, and deep assimilation of nuanced knowledge revolutionize business and society worldwide. This heralds a potential transition towards data-intensive economies, governments, industrial and social sectors. The accelerated pace of data processing, data intelligence, and analytics encompasses business intelligence, data sciences and machine learning. The spectrum of such upheavals and associated technological transitions is a watershed moment and impacts business and social transformations. In the paper on the role of data in the social realm (Technology as a catalyst for sustainable social business: Advancing the research agenda, 2019). Ashraf et al. opine that “Despite its immense potentials as a sustainable and innovative means to solve specific social problems, the basic concept of the social business model remains unclear to many”. In recent times there has been an inconsistent approach towards social business research. Subsequently, the contemporary business scenario is yet to optimally capitalize on the advantages of the Social Business concept and address the divergent socio-economic and ecological issues worldwide, with profits intact. This should in no way dilute profit maximization for optimizing socio-economic benefits for value creation and sustainability. “Although the social enterprise is often considered to have positive future potential, it is currently underdeveloped” (Bell, 2003). Therefore, social entrepreneurship should generate and ensure nuanced and effective innovations, addressing underserved needs. In contemporary times, tools harnessing Big Data are becoming widely applied, leading to a huge reservoir of untapped diverse data. This subsequently creates an immense opportunity to accelerate the use of Big Data towards social good and sustainability. Though this is a recent trend, it indeed holds promise in the post-COVID world that would be privy to unprecedented socio-economic upheavals and an increased need to address issues of humankind for greater global welfare. In the recent past, diverse data sets have created large-scale solutions in diverse spheres ranging from weather forecasts to airline tickets. Insightful correlations and Big Data go hand in hand and hold the key to several complicated pressing social issues. Therefore, a new crop of social entrepreneurs in public health, social welfare, and humanitarian relief would surely emerge by default. Therefore, it is all the more relevant to make sense of a deluge of Big Data towards alleviating a disease, ecological imbalance, war, and most importantly, the patterns to cope with the disease and its aftermath. This chapter proposes anticipating and predicting the immense possibilities of optimizing Big Data and digitization as key critical drivers of empirical simulation and troubleshooting. Good governance, inclusive society, elimination of corruption, and streamlining policy measures would emerge as default collectives of such social entrepreneurial ventures. The chapter would draw inferences from such models of socially inclined data analytics by data scientists, leading to relevant social models of significance. Implications of the chapter would be to assuage the fault lines, draw inferences from the past, and delve into the plausibility and relevance of Big Data to replicate and innovate socially relevant models to map bigger social issues. This, of course, should have embedded benchmarks of equality, ethics, and empowerment while processing Big Data for a greater social good in a post-COVID world gaping at us.

The world is witnessing an unprecedented disruption due to the COVID-19 pandemic in almost all spheres of socio-economic activity. This black swan moment is unprecedented since the advent of the Industrial Revolution. Simultaneously, there is a shift towards effective use of data and technology. There is an exponential increase in the quantum of data collected and has subsequently necessitated a paradigm shift in the functioning of Industry 4.0. Data intelligence, data science, artificial intelligence, machine learning, and deep assimilation of nuanced knowledge revolutionize business and society worldwide. This heralds a potential transition towards data-intensive economies, governments, industrial and social sectors. The accelerated pace of data processing, data intelligence, and analytics encompasses business intelligence, data sciences and machine learning. The spectrum of such upheavals and associated technological transitions is a watershed moment and impacts business and social transformations. In the paper on the role of data in the social realm (Technology as a catalyst for sustainable social business: Advancing the research agenda, 2019). Ashraf et al. opine that “Despite its immense potentials as a sustainable and innovative means to solve specific social problems, the basic concept of the social business model remains unclear to many”. In recent times there has been an inconsistent approach towards social business research. Subsequently, the contemporary business scenario is yet to optimally capitalize on the advantages of the Social Business concept and address the divergent socio-economic and ecological issues worldwide, with profits intact. This should in no way dilute profit maximization for optimizing socio-economic benefits for value creation and sustainability. “Although the social enterprise is often considered to have positive future potential, it is currently underdeveloped” (Bell, 2003). Therefore, social entrepreneurship should generate and ensure nuanced and effective innovations, addressing underserved needs. In contemporary times, tools harnessing Big Data are becoming widely applied, leading to a huge reservoir of untapped diverse data. This subsequently creates an immense opportunity to accelerate the use of Big Data towards social good and sustainability. Though this is a recent trend, it indeed holds promise in the post-COVID world that would be privy to unprecedented socio-economic upheavals and an increased need to address issues of humankind for greater global welfare. In the recent past, diverse data sets have created large-scale solutions in diverse spheres ranging from weather forecasts to airline tickets. Insightful correlations and Big Data go hand in hand and hold the key to several complicated pressing social issues. Therefore, a new crop of social entrepreneurs in public health, social welfare, and humanitarian relief would surely emerge by default. Therefore, it is all the more relevant to make sense of a deluge of Big Data towards alleviating a disease, ecological imbalance, war, and most importantly, the patterns to cope with the disease and its aftermath. This chapter proposes anticipating and predicting the immense possibilities of optimizing Big Data and digitization as key critical drivers of empirical simulation and troubleshooting. Good governance, inclusive society, elimination of corruption, and streamlining policy measures would emerge as default collectives of such social entrepreneurial ventures. The chapter would draw inferences from such models of socially inclined data analytics by data scientists, leading to relevant social models of significance. Implications of the chapter would be to assuage the fault lines, draw inferences from the past, and delve into the plausibility and relevance of Big Data to replicate and innovate socially relevant models to map bigger social issues. This, of course, should have embedded benchmarks of equality, ethics, and empowerment while processing Big Data for a greater social good in a post-COVID world gaping at us.

Social entrepreneurship is today an imperative across the world. The scarcity of resources, the needs of the growing population, and the growing burden on the environment have made people realize a need for organizations that can be profitable while bringing positive change in society and the environment. Sustainable, out-of-the-box solutions are required for solving social challenges. The onus of providing these solutions are often taken by aspiring entrepreneurs, who see opportunity in the challenges and are willing to take risks to create innovative and effective solutions for society’s benefit. The chapter elucidates the meaning of social entrepreneurship, the difference between commercial and social entrepreneurship, the models of social enterprises in practice, and the recommended methodology to evaluate social impact. The chapter features international case studies (through secondary research) and cases about social entrepreneurs who have dramatically improved the lives of people while being financially sustainable – an organization like SELCO Solar Lights Private Ltd, ARMAAN, Yellow Leaf and Arvind Eye Hospital, and many others have provided solutions for the economically weaker section of society. Many of these new-age social entrepreneurs use Big Data and artificial intelligence to ensure that their initiatives create greater social change. Some of these initiatives are highlighted in the chapter.

Social entrepreneurship is today an imperative across the world. The scarcity of resources, the needs of the growing population, and the growing burden on the environment have made people realize a need for organizations that can be profitable while bringing positive change in society and the environment. Sustainable, out-of-the-box solutions are required for solving social challenges. The onus of providing these solutions are often taken by aspiring entrepreneurs, who see opportunity in the challenges and are willing to take risks to create innovative and effective solutions for society’s benefit. The chapter elucidates the meaning of social entrepreneurship, the difference between commercial and social entrepreneurship, the models of social enterprises in practice, and the recommended methodology to evaluate social impact. The chapter features international case studies (through secondary research) and cases about social entrepreneurs who have dramatically improved the lives of people while being financially sustainable – an organization like SELCO Solar Lights Private Ltd, ARMAAN, Yellow Leaf and Arvind Eye Hospital, and many others have provided solutions for the economically weaker section of society. Many of these new-age social entrepreneurs use Big Data and artificial intelligence to ensure that their initiatives create greater social change. Some of these initiatives are highlighted in the chapter.

This chapter considers the role of universities in stimulating social innovation, and in particular the issue that despite possessing substantive knowledge that might be useful for stimulating social innovation, universities to date have not been widely engaged in social innovation activities in the context of Quadruple Helix developmental models. We explain this in terms of the institutional logics of engaged universities, in which entrepreneurial logics have emerged in recent decades, that frame the desirable forms of university-society engagement in terms of the economic benefits they bring. We ask whether institutional logics could explain this resistance of universities to social innovation. Drawing on two case studies of universities sincerely committed to supporting social innovation, we chart the effects of institutional logics on university-supported social innovation. We observe that there is a “missing middle” between enthusiastic managers and engaged professors, in which four factors serve to undermine social innovation activities becoming strategically important to HEIs. We conclude by noting that this missing middle also serves to segment the operation of Quadruple Helix relationships, thereby undermining university contributions to societal development more generally.

This chapter considers the role of universities in stimulating social innovation, and in particular the issue that despite possessing substantive knowledge that might be useful for stimulating social innovation, universities to date have not been widely engaged in social innovation activities in the context of Quadruple Helix developmental models. We explain this in terms of the institutional logics of engaged universities, in which entrepreneurial logics have emerged in recent decades, that frame the desirable forms of university-society engagement in terms of the economic benefits they bring. We ask whether institutional logics could explain this resistance of universities to social innovation. Drawing on two case studies of universities sincerely committed to supporting social innovation, we chart the effects of institutional logics on university-supported social innovation. We observe that there is a “missing middle” between enthusiastic managers and engaged professors, in which four factors serve to undermine social innovation activities becoming strategically important to HEIs. We conclude by noting that this missing middle also serves to segment the operation of Quadruple Helix relationships, thereby undermining university contributions to societal development more generally.

The failure of the deterritorialised innovation policy addressing the regions based on the “one-size-fits-all” policymaking made the Research and Innovation Strategies for Smart Specialisation (RIS3) become the Holy Grail of the European cohesion. This policy strategy is part of a multilevel framework, which encompasses national and regional vectors harmonising transversal strategies and combining different aspects to generate a consistent policy mix. This growth strategy will reinforce the existence of an innovative and knowledge-based society, which aims to raise welfare, promote responsible practices, modernise economic activity and spread prosperity.Sustainable growth will optimise the use of resources, boost the efficiency levels, generate competitiveness and respect the environment. Inclusive growth will promote social and territorial cohesion which is sought after in the convergence policy, which has slowed down the pace after the financial crisis.The development of regional competitive advantages will rely on the establishment of relevant linkages between the Academia and the private institutions in knowledge creation and transfer. In this vein, the University is expected to play a central role, facing important challenges and requiring transformations, mostly in the case of less favoured regions.Productivity raise, construction of comparative advantages, market consolidation and profit maximisation, required to avoid the obsolescence of firms, will rely in the prosecution of innovative activities. Despite being risky, these activities are sought by firms as a source of economic performance increase, being the building blocks of a profit maximisation strategy. The velocity at which innovation occurs will differ among industrial sectors due to their singularities along with other firm structural characteristics, still, those who perform innovative activities are more prone to achieve higher standards of turnover growth and profits. The organisational competences concerning human capital, knowledge absorption, accumulation and diffusion will enhance the innovation capabilities, thus generating advantages. In this path, Universities will be determinant as they may leverage the success of the entrepreneurial innovativeness throughout the provision of relevant knowledge, productive techniques and methods. Absorbing, transforming and exploiting the general knowledge provided by the University will be the firms’ incumbency which will reflect the speed and the success of the individual’s innovative performance. Considering the reinforced role of the Academia as a knowledge producer and therefore inside the innovation process, the existence of incipient connections with firms will be unbearable.What enables and hinders University-firm linkages is, so far, overlooked in the literature demanding for the comprehensive analysis, in particular the causes of its failure, and the accurate policy mix that overcome the situation is vital for a successful RIS3.The singularities of this policy framework require redirection of the tools and actions to be taken such as incentives, grants, loans and subsidisation strategies. Empirical results shed light to the significant difference observed in the classification of the University as a source of information for innovation between public monies recipients and other firms. Among public funding beneficiaries, the Academia is an important source of knowledge to draw upon; conversely, for the other firms, it seems of poor importance the knowledge conveyed in the contact. In general, firms fail to consider the University as a relevant source of information for innovation, which seems to be incompatible with the establishment of smart specialisation strategies.These unexplored connections, which pledge the success of the present innovation policy, and reinforce the importance of its appraisal to fully understand the determinants of University-firm linkages and its connection to public subsidisation, encompassing the identification of the most effective beneficiaries. The econometric estimations, relying on the CIS, were run considering a panel of firms operating in Portugal, which provides the empirical evidence for a moderate innovation milieu which is poorly done so far as most of the studies focus on innovation leader.The findings reinforce the existence complementarities among policy instruments and highlight that new avenues of research should explore other policy instruments such as open innovation frameworks.

This chapter examines the close relationships between philanthropy and innovation. A case study of philanthropy improving eyesight in Africa is provided. The crucial impact of philanthropy on science and universities is discussed. A case study of how philanthropy fundamentally changed Queensland University is provided. The intimate relationship between philanthropy and the arts is explored. A case study is provided of the impact of philanthropy on a major arts institution. The connections between philanthropy and social and humanitarian innovation is described.

This chapter examines the close relationships between philanthropy and innovation. A case study of philanthropy improving eyesight in Africa is provided. The crucial impact of philanthropy on science and universities is discussed. A case study of how philanthropy fundamentally changed Queensland University is provided. The intimate relationship between philanthropy and the arts is explored. A case study is provided of the impact of philanthropy on a major arts institution. The connections between philanthropy and social and humanitarian innovation is described.

L’innovation sociale est largement considérée comme vertueuse. Cependant, le consensus qui semble régner en la matière vient de ce que les représentations et les pratiques englobées sous ce terme recouvrent un faisceau très diversifié d’approches et de réalités. Cette polysémie permet à de nombreux auteurs de se ranger sous une même bannière alors qu’ils ont des références et des orientations distinctes, voire divergentes. L’éloge unanime de l’innovation sociale ne saurait donc faire illusion. À cet égard, un travail introductif autour de l’innovation sociale a mis en évidence deux acceptions contrastées. La première version, qui peut être qualifiée de faible, aménage le système existant, insiste sur l’importance de l’épreuve marchande et valorise l’entreprise privée dans sa capacité à trouver de nouvelles solutions aux problèmes de société. La seconde version, qui peut être désignée comme forte, affiche une visée transformatrice ; elle prône, en réaction à la démesure du capitalisme marchand, une articulation inédite entre pouvoirs publics et société civile pour répondre aux défis écologiques et sociaux. La première se contente d’une amélioration du modèle économique dominant, l’innovation s’inscrivant dans une perspective réparatrice et fonctionnelle, tandis que la seconde a pour caractéristique un questionnement critique de ce modèle, et a pour horizon une démocratisation de la société.

L’innovation sociale est largement considérée comme vertueuse. Cependant, le consensus qui semble régner en la matière vient de ce que les représentations et les pratiques englobées sous ce terme recouvrent un faisceau très diversifié d’approches et de réalités. Cette polysémie permet à de nombreux auteurs de se ranger sous une même bannière alors qu’ils ont des références et des orientations distinctes, voire divergentes. L’éloge unanime de l’innovation sociale ne saurait donc faire illusion. À cet égard, un travail introductif autour de l’innovation sociale a mis en évidence deux acceptions contrastées. La première version, qui peut être qualifiée de faible, aménage le système existant, insiste sur l’importance de l’épreuve marchande et valorise l’entreprise privée dans sa capacité à trouver de nouvelles solutions aux problèmes de société. La seconde version, qui peut être désignée comme forte, affiche une visée transformatrice ; elle prône, en réaction à la démesure du capitalisme marchand, une articulation inédite entre pouvoirs publics et société civile pour répondre aux défis écologiques et sociaux. La première se contente d’une amélioration du modèle économique dominant, l’innovation s’inscrivant dans une perspective réparatrice et fonctionnelle, tandis que la seconde a pour caractéristique un questionnement critique de ce modèle, et a pour horizon une démocratisation de la société.

Face à la conception technocratique et entrepreneuriale portée par les pouvoirs publics, une approche alternative de l’innovation sociale, plus populaire et moins visible, à travers l’exploration d’initiatives citoyennes. Prenant comme point de départ le constat d’une appropriation institutionnelle de l’innovation sociale, orientée vers la compétitivité et l’efficacité marchande des expériences de l’économie sociale et solidaire, l’ouvrage vise à la fois à apporter un regard critique sur cette conception de l’innovation sociale et à remettre en lumière des expérimentations citoyennes peu prises en compte par les pouvoirs publics. Il montre ainsi la nécessité d’un tournant épistémologique valorisant les dynamiques de coproduction des savoirs et des politiques entre acteurs, chercheurs et institutions.

Face à la conception technocratique et entrepreneuriale portée par les pouvoirs publics, une approche alternative de l’innovation sociale, plus populaire et moins visible, à travers l’exploration d’initiatives citoyennes. Prenant comme point de départ le constat d’une appropriation institutionnelle de l’innovation sociale, orientée vers la compétitivité et l’efficacité marchande des expériences de l’économie sociale et solidaire, l’ouvrage vise à la fois à apporter un regard critique sur cette conception de l’innovation sociale et à remettre en lumière des expérimentations citoyennes peu prises en compte par les pouvoirs publics. Il montre ainsi la nécessité d’un tournant épistémologique valorisant les dynamiques de coproduction des savoirs et des politiques entre acteurs, chercheurs et institutions.

La transformation numérique et l’innovation collaborative ou les notions associées « d’intelligence collective », de « design thinking », « d’agilité » sont en passe de devenir les principaux concepts à la mode du management dans les organisations privées et publiques, au moins au sein des sièges et des directions centrales. Partant d’une description des spécificités des bouleversements introduits par la transition numérique et des technologies capacitantes qu’elle promeut, nous montrons comment les opérateurs sont parfois en demande de plus d’innovations numériques pour améliorer leurs conditions de travail et les services rendus au public, pour autant que celles-ci ne soient pas substitutives et excessivement rationalisantes. Les démarches d’innovation collaborative, soutenues par le haut management de manière parfois paradoxale, contribuent à faciliter ces mutations.

La transformation numérique et l’innovation collaborative ou les notions associées « d’intelligence collective », de « design thinking », « d’agilité » sont en passe de devenir les principaux concepts à la mode du management dans les organisations privées et publiques, au moins au sein des sièges et des directions centrales. Partant d’une description des spécificités des bouleversements introduits par la transition numérique et des technologies capacitantes qu’elle promeut, nous montrons comment les opérateurs sont parfois en demande de plus d’innovations numériques pour améliorer leurs conditions de travail et les services rendus au public, pour autant que celles-ci ne soient pas substitutives et excessivement rationalisantes. Les démarches d’innovation collaborative, soutenues par le haut management de manière parfois paradoxale, contribuent à faciliter ces mutations.

La co-construction est un processus par lequel des acteurs différents confrontent leurs points de vue et s’engagent dans une transformation de ceux-ci jusqu’au moment où ils s’accordent sur des traductions qu’ils ne perçoivent plus comme incompatibles. Ce moment particulier est celui où ils pensent avoir défini un « monde commun » qui va fonder leur compromis ; ils pourront alors poursuivre leur coopération afin de construire un projet d’action commun et réfléchir ensemble à sa mise en œuvre. La notion de co-construction s’est largement diffusée dans le monde académique et non académique. Cependant, sa définition reste encore aujourd’hui incertaine et fait l’objet de propositions dans la littérature grise des dossiers, finalisée par des institutions (certains conseils généraux, entre autres) ou des cabinets conseil. Pratiquement aucun dictionnaire de sociologie ou de sciences humaines ne la définit à l’exception du Dictionnaire de la participation . « Le terme co-construction est devenu depuis quelques années très en vue. Il se retrouve dans beaucoup d’articles et livres à portée académique. L’univers professionnel n’en est pas moins en reste où cette approche de gestion semble l’un des moyens pour pérenniser la performance des organisations. Néanmoins, lorsque l’on s’y attarde un peu plus en profondeur, on constate qu’il est davantage cité que conceptualisé. Très peu d’auteurs s’y sont réellement attardés ». C’est une notion ambiguë et la proximité avec des notions voisines plus académiques comme la coopération n’est sans doute pas une condition facilitatrice pour une explicitation de ses dimensions propres…

La co-construction est un processus par lequel des acteurs différents confrontent leurs points de vue et s’engagent dans une transformation de ceux-ci jusqu’au moment où ils s’accordent sur des traductions qu’ils ne perçoivent plus comme incompatibles. Ce moment particulier est celui où ils pensent avoir défini un « monde commun » qui va fonder leur compromis ; ils pourront alors poursuivre leur coopération afin de construire un projet d’action commun et réfléchir ensemble à sa mise en œuvre. La notion de co-construction s’est largement diffusée dans le monde académique et non académique. Cependant, sa définition reste encore aujourd’hui incertaine et fait l’objet de propositions dans la littérature grise des dossiers, finalisée par des institutions (certains conseils généraux, entre autres) ou des cabinets conseil. Pratiquement aucun dictionnaire de sociologie ou de sciences humaines ne la définit à l’exception du Dictionnaire de la participation . « Le terme co-construction est devenu depuis quelques années très en vue. Il se retrouve dans beaucoup d’articles et livres à portée académique. L’univers professionnel n’en est pas moins en reste où cette approche de gestion semble l’un des moyens pour pérenniser la performance des organisations. Néanmoins, lorsque l’on s’y attarde un peu plus en profondeur, on constate qu’il est davantage cité que conceptualisé. Très peu d’auteurs s’y sont réellement attardés ». C’est une notion ambiguë et la proximité avec des notions voisines plus académiques comme la coopération n’est sans doute pas une condition facilitatrice pour une explicitation de ses dimensions propres…

This chapter is about evidence and whether we can, or should, know our impact, the effect we have in the world. It addresses the difficulties as well as the possibilities of evidence for innovators and politicians, civil servants and head teachers, charities and doctors. I also touch on the question at the level of daily life, the moral question of whether we help those around us to be healthier, happier and more prosperous. Knowing our own impacts is, I argue, as much a moral prerogative as the traditional philosophical injunction of knowing ourselves.The enlightenment storyMany of us imbibed from an early age what can be called the enlightenment story. In this story new knowledge is steadily accumulated, mainly in universities and from academic journals. Theories are invented, tested, refuted and then improved. Scepticism helps to refine them and, as Wittgenstein wrote, the child first learns belief and only then learns doubt. You could say that at school we learn knowledge, and then at university we learn to question that knowledge.Belief is strengthened precisely because it has already been knocked down. And so, accumulating knowledge shows that this medicine, that economic policy or this teaching method works and many others don’t. The successful method then spreads, because when you design a better mousetrap the world beats a path to your door. It spreads because people are rational and want to do better and are persuaded by evidence. And so, the world progresses. Light replaces darkness. Effective solutions displace failed ones.It's easy to mock the enlightenment story. The sociologists of science have shown a much messier pattern of change – full of barriers, wilful resistance and peer pressure. But the old enlightenment story contains a good deal of truth and is preferable to the alternatives. Because of intense pressures to act on evidence, and habits of doubt among maintenance staff and engineers, aircraft do not drop out of the sky. Smoking made the slow progress from evidence of harm, through taxes and warnings to full-scale bans, and millions of lives were saved.Experimental methods have been used for many decades.

This chapter is about evidence and whether we can, or should, know our impact, the effect we have in the world. It addresses the difficulties as well as the possibilities of evidence for innovators and politicians, civil servants and head teachers, charities and doctors. I also touch on the question at the level of daily life, the moral question of whether we help those around us to be healthier, happier and more prosperous. Knowing our own impacts is, I argue, as much a moral prerogative as the traditional philosophical injunction of knowing ourselves.The enlightenment storyMany of us imbibed from an early age what can be called the enlightenment story. In this story new knowledge is steadily accumulated, mainly in universities and from academic journals. Theories are invented, tested, refuted and then improved. Scepticism helps to refine them and, as Wittgenstein wrote, the child first learns belief and only then learns doubt. You could say that at school we learn knowledge, and then at university we learn to question that knowledge.Belief is strengthened precisely because it has already been knocked down. And so, accumulating knowledge shows that this medicine, that economic policy or this teaching method works and many others don’t. The successful method then spreads, because when you design a better mousetrap the world beats a path to your door. It spreads because people are rational and want to do better and are persuaded by evidence. And so, the world progresses. Light replaces darkness. Effective solutions displace failed ones.It's easy to mock the enlightenment story. The sociologists of science have shown a much messier pattern of change – full of barriers, wilful resistance and peer pressure. But the old enlightenment story contains a good deal of truth and is preferable to the alternatives. Because of intense pressures to act on evidence, and habits of doubt among maintenance staff and engineers, aircraft do not drop out of the sky. Smoking made the slow progress from evidence of harm, through taxes and warnings to full-scale bans, and millions of lives were saved.Experimental methods have been used for many decades.

Technology is the answer, but what was the question? Introduction Many firms, charities and governments are in favour of more innovation, and like to side with the new against the old. But should they? A moment's reflection shows that it's not altogether coherent (whether intellectually, ethically or in terms of policy) to simply be in favour of innovation, whether that innovation is a product, a service or a social idea. Some innovations are unambiguously good (like penicillin or the telephone). Others are unambiguously bad (like concentration camps or nerve gas). Many are ambiguous. Pesticides kill parasites but also pollute the water supply. New surveillance technologies may increase workplace productivity but leave workers more stressed and unhappy. Smart missiles may be good for the nations deploying them and terrible for the ones on the receiving end.In finance, Paul Volcker, former head of the US Federal Reserve, said that the only good financial innovation he could think of was the automated teller machine. That was an exaggeration. But there is no doubt that many financial innovations destroyed more value than they created, even as they enriched their providers, and that regulators and policy makers failed to distinguish the good from the bad, with very costly results. In technology, too, a similar scepticism had emerged by the late 2010s, with digital social media described as the ‘new tobacco’, associated with harm rather than good, with addiction rather than help. Or, to take another example: when the US Central Intelligence Agency's venture capital arm, In-QTel, invested heavily in firms like Palantir, which then became contractors for the intelligence and military (a prime example of the ‘entrepreneurial state’), it was far from obvious how much this was good or bad for the world.The traditional justification for a capitalist market economy is that the net effects of market-led innovation leave behind far more winners than losers, and that markets are better able to pick technologies than bureaucracies or committees. But even if, overall, the patterns of change generate more winners than losers, there are likely to be some, perhaps many, cases where the opposite happens. It would be useful to know.