Computational and Data-Driven Gellnerian Social Theory: From the Transition to Modernity to a Disenchanted Future

Abstract

Computational and data-driven approaches in social science have become increasingly popular in recent years, in tandem with the rise of artificial intelligence. Gellner’s social theory may seem far removed from these approaches, but in fact the clarity of his analytical and comparative-historical account of social change lends itself to formalization and data-driven testing, thus allowing evaluation in relation to rival social theories. Gellner’s social thought also embraced empiricism and mechanism, which aid causal explanations and so contribute to cumulative social scientific knowledge. This chapter examines Gellner’s social theory with a view to how it fares when put to the test of recent computational social science approaches, which include visualizing causal pathways. A starting point here is Gellner’s conception of the transition to modernity, which has been much discussed, where he aims to pinpoint the main explanations. And one example of how his thought can inform analysis of the future of modernity is climate change, where current trends will, without major course corrections, inexorably lead to greater coercion. Gellner did not anticipate data-driven computational approaches, but his thought provides important guideposts for cumulation in keeping with this new turn in the social sciences.

Comment on Ralph Schroeder’s ‘Computational and Data-Driven Gellnerian Social Theory: From the Transition to Modernity to a Disenchanted Future’

Siniša Malešević

University College Dublin, Dublin, Ireland

sinisa.malesevic@ucd.ie

It is always a pleasure to comment on Ralph Schroeder’s work. He is a very unusual scholar in the sense that he successfully combines his vast knowledge of sociological theory with his expertise in computational social science. Most computational social scientists do not engage with social theory, while an overwhelming majority of social theorists do not know anything about computational social science. Furthermore, he is an innovative social theorist who has made important contributions to understanding the role of science and technology in modernity (Schroeder 2013). This is why Schroeder’s insights on the relationship between the two are so important.

As I am not an expert on computational social science, my comments here are rather general and amateurish. However, I hope that they can still be useful in probing the long-term consequences of using artificial intelligence (AI) in the development of formal social theory. In this short comment I would like to pinpoint several potential problems with relying solely on AI as a tool for developing more predictive and cumulative social theories.

First, one of the key advantages of AI is that a machine can learn and teach itself to improve upon past imperfections, and as such can develop better explanatory models for a variety of existing problems. In this sense, AI could potentially do more and better than ordinary human beings and could create a more advanced formal social theory. However, this raises the key question: Is there enough flexibility built into AI to accommodate contingencies, unintended consequences of human action, long-term historical legacies, and different behavioural patterns of human beings? For example, we act differently as individuals, as members of small groups, and as representatives of large-scale institutions and social organizations (Simmel 1964; Collins 2004). Can AI anticipate and deal with all these differences? Are algorithms malleable enough to accommodate all these complexities of human action?

Second, formalized theory may not be open to new avenues of thinking. It seems to me that AI modelling is better suited for positivist approaches and has less to offer to two other main traditions of research—interpretative and critical approaches. If AI models conceive causality in very narrow terms, they cannot be very useful in advancing alternative forms of sociological theorizing. It seems to me that the Weberian tradition of historical and interpretative analysis is not well suited to algorithmic thinking. This is even more the case with the critical and emancipatory tradition of research as developed in Marxist, feminist and critical race approaches. Would positivism then become the only game in town? If this is the case, it would certainly impoverish sociological theory. Furthermore, the ultimate dissatisfaction with AI formal theory might lead to a situation that resembles the academic discontent associated with the 1920s logical positivist movement. This movement also promised the end of all ‘metaphysical doctrines’ and offered the only scientific pathway towards acquiring knowledge, but within a few decades, this attempt was largely abandoned, and the movement was proclaimed to be ‘dead, or as dead as a philosophical movement ever becomes’ (Passmore 1967: 1). Consequently, the leading representatives of the movement, such as L. Wittgenstein, shifted to another extreme and became relativist philosophers.

Third, the sole reliance on AI as the tool for formal social theory might also impact on the quality of knowledge. Although science advances through cumulative knowledge, there is a pronounced difference in the way knowledge is generated and analysed in social science as opposed to natural science. In this sense, AI might not be able to deal with issues of reflexivity in social science. Historical knowledge is regularly contested and periodically reinterpreted as it responds to changing social realities and the availability of new interpretative frameworks. There are simply too many variables involved here to be accommodated by algorithmic models. Social scientists and historians continue to disagree over key concepts, variables, definitions and theoretical approaches, and it is difficult to see how AI could attain a consensus on these issues.

Fourth, while AI modelling might work well in some areas of science, the social scientists, historians and scholars from the humanities are unlikely to ever agree fully on what constitutes indisputable evidence. For example, archaeologists and palaeontologists who collect data on human remains in prehistory still cannot agree on how old human violence is. The remnants of broken human skulls and bones could be interpreted very differently—either as a violent human-on-human attack, or as a consequence of debilitating disease, or as an environmentally caused death and so on (Malešević 2017). Similarly, relying on proxy variables, such as the Gini index, the size of militaries, or military mobilization rates, which Schroeder mentions in his chapter, can be equally contested, as they can never fully capture the complexity of social and historical processes. For example, in Wimmer’s (2012) analysis of the impact that warfare had on nation-state formation, he uses the extent of railroad tracks as a proxy for industrialization. This measurement is in part deployed to disprove Gellner’s theory of nationalism. However, such a proxy measure is completely misleading, for some societies, such as Ireland or India, which had very extensive railroad networks that were built during the British rule, were mostly agricultural societies that had no industry outside of their main cities (Malešević 2013).

Fifth, another important issue that concerns the potential dominance of AI modelling in formal social theory relates to its long-term social consequences. Leaving sociological theorizing to machines would have significant and potentially harmful effects on society. The models could be manipulated to suit a variety of non-sociological causes involving potential political and economic misuses. If AI theory modelling becomes the most significant or only legitimate from of theorizing, would this abolish all other forms of theorizing and thus lead to some kind of ultimate truth—a new AI religion? Will this be the death of social theory? All of this sounds very problematic to me.

Sixth, AI modelling in sociological theory might also have long-term repercussions in terms of politics. Different political orientations often underpin different theoretical perspectives, and reducing everything to formalized logarithms might signal the end of political plurality. As human beings are political beings, it is impossible to envisage society without politics. So, the question remains as to how to reach a consensus on key theoretical questions without having a consensus on key political questions. In this I am more sympathetic to the views of Gellner and Collins, both of whom emphasize the importance of maintaining very different traditions of thinking and analysis. Gellner (1994) dedicated much of his academic career to understanding worldviews that were different from his own (nationalism, Islam, Soviet Marxism, etc.). Similarly, Collins (1998) has historically analysed different philosophical traditions and has found that new ideas and new ways of thinking develop only in the context of highly intensified competition between six to eight different traditions of thought.

I would also like to briefly raise another two minor points that have been discussed in Schroeder’s chapter. First, both Schroeder and Gellner emphasize the importance of cognition and techno-science in the contemporary world. However, it is difficult to see how science and technology can be neatly separated from coercion and production. For science to have a profound and long-term impact on society it has to be organized and ideologically framed so as to make a wide appeal. As we all know, many scientific discoveries and major technological breakthroughs were made long before modernity. Yet they were not implemented until there was a substantial organizational and ideological change that allowed for these discoveries to be recognized widely as being socially meaningful. So political, coercive and ideological powers have all historically played a crucial role in making science and technology legitimate and socially relevant.

Second, Schroeder is right that techno-science is indispensable in understanding and dealing with the immediate problem of environmental destruction. In this context he makes reference to a witty statement that ‘the laws of geophysics are indifferent to politics’. However, this statement works in reverse too, as most politicians tend to ignore medium and long-term environmental threats. So, for them, political life is largely indifferent to the laws of geophysics. This suggests that political and coercive-military powers remain the ultimate arbiter in environmental change. Even though environmental destruction is obviously a global problem that requires global solutions, the dominant political-territorial structure of the contemporary world (i.e. the nation-state system) prevents politicians from thinking and acting beyond their own states. Consequently, the most common reactions involve protection of one’s own citizens.

Despite these amateurish remarks, I would like to emphasize that Schroeder has written an excellent and thought-provoking chapter that offers Gellnerian scholars a way forward in the twenty-first century where, whether we like it or not, AI and computational social science are likely to dominate much of social research.

References

Collins, R. 2004. Interaction ritual chains. Princeton: Princeton University Press.

Collins, R. 1998. The sociology of philosophies: A global theory of intellectual change. Cambridge: Harvard University Press.

Gellner, Ernest. 1994. Conditions of liberty: Civil society and its rivals. London: Hamish Hamilton.

Malešević, Siniša. 2017. The rise of organised brutality: A historical sociology of violence. Cambridge: Cambridge University Press.

Malešević, Siniša. 2013. Making boundaries and fighting wars: Ethnicities, nation-states, and empires. European Journal of Sociology 54 (3): 507–514.

Passmore, John A. 1967. Logical positivism. In The encyclopedia of philosophy, ed. P. Edwards, 5: 52–57. New York: Macmillan.

Schroeder, Ralph. 2013. An age of limits: Social theory for the 21st century. New York: Palgrave.

Simmel, Georg. 1964 [1908]. In The sociology of Georg Simmel, ed. K. Wolff. Glencoe: Free Press.

Wimmer, A. 2012. Waves of war: Nationalism, state formation, and ethnic exclusion in the modern world. Cambridge: Cambridge University Press.