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The purpose of this section is to reflect upon ontological and epistemological issues relevant to the research on business rates. To achieve this purpose, this chapter begins with an overview of how big data has shifted paradigms (Section 6.1.1). This is followed by a

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review of issues within economics so that the relative merits of post-positivism and positivism can be weighted (Section 6.1.2) to choose the most suitable epistemological stance for this research project.

6.1.1 Big Data

Economics and other social sciences have experienced a significant shift in computational nature. As a result, there have been continued discussions about the change in paradigms. Kuhn (1962) emphasises that the worldview and knowledge exist at one moment in time. In contrast to Kuhn’s perception that paradigms are evolving because of particular phenomena, Hey et al. (2009), inspired by Jim Gray, demonstrate that paradigms advance because of data. They classify four paradigms according to the period and term the fourth exploratory science. They establish that data-intensive statistical exploration and data mining are today’s science. Similarly, others (Kelling et al., 2009; Miller, 2010) have argued that data-driven science should form a new paradigm because this type of epistemology could help to extract new knowledge. Furthermore, an increasing number of researchers (Bollier, 2010; Floridi, 2012; Mayer-Schonberger and Cukier, 2013) have claimed that big data should affect how business is conducted, knowledge shaped, and governance enacted. For instance, Kitchin (2014) highlights the opinions of several professionals (Anderson, Prenky, Dyche, and Clark) who have claimed that big data could solve challenges even without theories. They reason that big data could create more comprehensive and extensive interdisciplinary studies that would be less limited to theories.

6.1.2 Positivism versus Post-positivism

In the period from Comte, through logical positivism and the Vienna Circle, to critical rationalism and operationalism, philosophy of knowledge in economics has experienced numerous variations and modifications. Post-positivism has become increasingly popular in economics. It has incorporated ideas of Feyerabend’s methodological pluralism, falsificationism (Popper), and fallibilism (Hetherington, 2000).

The central practical implication would be an assumption that sophisticated statistical methods are not sufficient to create scientific knowledge. Post-positivists have maintained that social sciences need more deliberative and integrated methods. On the other hand, the advantage of abundant information could create some valuable knowledge. For instance, Duranton’s et al. (2011) methodology relies on secondary data and improves the understanding of business rates. This understanding could be as equally significant as interviews, for example, from Mehdi’s (2003) study, or theoretical discussions about the possible effects on business rates such as those by Williams (2011) or Smith (2015). It may

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be further questioned whether the compound understanding which could not be incorporated in today’s relatively sophisticated statistical modelling with big data (Kitchin, 2014) is required to answer the calls for evidence concerning business rates (Bond et al., 2013; HM Treasury, 2015 and 2016; Muldoon-Smith and Greenhalgh, 2015).

A critique of positivism could be valuable since it is often oriented towards a more comprehensive and elaborate description of a phenomenon and the relationships within it.

Some of these issues were discussed by von Mises. In 1933, a period when the social sciences and economic policy were experiencing confusion, von Mises argued that the core intellectual errors of protectionism, socialism, statism, racism and irrationalism were against economic logic. This was also identified by other scholars who criticised the reductionist nature of positivism owing to such factors as an absence of neutral knowledge, dualistic thinking (Henriques et al., 1998), ethical aspects (Schratz and Walker, 1995) and a focus on statistical approaches (Cradwell, 1980). This implies that the easy to understand deductionist models similar to those presented in the Theory Review Chapter should be looked at with caution. They provide a good indication for the majority of cases, but might not be accurate for all cases.

6.1.3 Theory-driven versus Data-driven

As computing power and the amount of information have grown, complex techniques focusing on data-driven modelling can provide an alternative to predetermined and reductionist models. The contradiction that could potentially arise is that data evaluation provides legitimate results without them being subjected to theory restrictions and is applied with less explicit criteria. Whilst in the theory-driven standard positivist modelling, data evaluation is legitimised in the context of a well-formed theory. The data-driven techniques, such as REEM trees, do not require a theory base and can deal with the previously mentioned shortcomings and methodological errors inherent in neoclassical economics (Adam and Westlund, 2013). Thus, the results are expected to be more dynamic, complex and sophisticated (Kitchin, 2014:3).

However, it could be erroneous to focus only on data. Although data-driven modelling does not impose additional assumptions, it still requires contextualisation on existing knowledge; it may be limited in scope and may produce only one kind of knowledge (Crampton et al., 2012). While these algorithms enable rich representations of complex economic systems and advanced reasoning capabilities, a key challenge is finding the right balance between leveraging computational resources and applying theory because the data may capture interactions that do not necessarily provide meaningful insights into the questions addressed. Having said that, the whole data about a phenomenon can be included

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in the modelling and even those irrational, according to our current understanding, interactions may be worth acknowledging.

As a result, this research project follows the paradigm of positivism but employs both data-driven and theory-driven modelling in an attempt to gather a complete understanding of the phenomenon. Given all criticism of positivism, it may seem reasonable to suggest that data-driven modelling could at least partly reduce the drawbacks of positivism. The availability of both larger datasets and more sophisticated techniques should result in greater complexity. Athough Hutchison's (1941) positivist seeks to reduce a phenomenon to universal and abstract principles and tends to fragment human behaviour, data-driven reasoning may help to act against this reductionist nature by including unpredetermined relationships within the analysis.

Overall, as a researcher, I adopt the belief that the world of social interactions exists independently of what I perceive it to be; it is a broadly rational, external entity and responsive to scientific modes of inquiry. Also, I believe that data analysis may be useful both before and after theory development. The combination of both could not only advance the knowledge of Small Business Rate Relief but also answer recent calls for evidence concerning this topic.