Manuscript Topics

Social sciences cover a very wide range of topics for the ultimate goal of understanding how people make decisions, how organizations appear and evolve, how people and organizations interact with each other, among others. However, due to a lack of appropriate tools for conducting rigorous reasoning, most theories in social sciences are developed on the bases of conjectures or hypotheses, suggested either empirically or anecdotally. That explains why there are many long-lasting debates in the community of social science scholars that have been reciprocating back and forth in circles without producing many, if any at all, convincing results. That is also the reason why conclusions, derived out of the same set of social phenomena, can often be inconsistent with or even contradictory to each other.

One characteristic of the problems studied in social sciences is that each social phenomenon under consideration tends to be large scale, complex, and involve interacting components. That represents a challenge to the methodologies commonly employed in social sciences and reveals a fertile ground for introducing additional mathematical reasoning, computational models and statistical analysis into the investigations of social science topics. On top of such realization, this special issue is planned to attract some of the recent, state-of-art developments in social sciences, where key concepts and main conclusions are developed and derived on the bases of mathematical, computational and/or statistical models, reasonings and analyses. Potentially, inconsistencies in conclusions can be maximally avoided and novel, while practically useful, results can be established.

To this end, this special issue invites scholars to contribute their best works on social science topics that are rooted in the rigor of either mathematical, or computational or statistical analyses. Jointly, we will be able to help reduce the sharp difference between studies of mathematics/natural science and those of social sciences. In particular, the former creates scientifically sound conclusions that can be widely employed to the design and production of useful commercial goods, while the latter derives theories, from isolated events and processes, that cannot generally be applied successfully to different situations or different times. To see what has caused the difference, one only needs to note how mathematics/natural science and social sciences develop their respective conclusions. Specifically, mathematics and natural science derive conclusions based on a few postulates or laws through using rigorous logical reasoning, while studies in social sciences tend to be data and anecdotes driven through using econometrical models. Even for theoretical studies in social sciences, they generally derive conclusions based on earlier empirical discoveries, which, of course, do not constitute a solid ground on which to develop reliable conclusions. Therefore, speaking methodologically, there appears a strong need for scholars to introduce as much of the full spectrum of mathematical, computational and statistical reasonings into the studies of social sciences as possible.

Another important realization for why we need to promote more heavily the introduction of mathematical, computational and statistical reasonings into social sciences is the observation of the clear parallelism between the current era of big data and that when Isaac Newton and Gottfried Wilhelm Leibniz formally launched calculus in the 17th century. In other words, centuries ago when deluges of astronomical data became available, instead of directly diving into the analysis of the data, these great minds first developed calculus and then formally derived the laws that were uncovered empirically by others from exploring the astronomical data. If the history more or less repeats itself, we then currently live in the golden moment of time when new theoretical discoveries and establishments can be expectedly made and constructed along with thousands of others who are busy with empirical studies of big data.

Key words: competitive advantage; computational approach; consistent conclusions; decision making; emergent macro-property; interacting agents; mathematical modeling; micro-foundation; organizational network; reliable theory; social phenomenon; statistical reasoning; systemic thinking

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