In this section I look at real-life manifestations of complexity in social sys-tems. As often mentioned in the previous pages, reality is rich in examples of complexity in all sorts of domains, ranging from the physical laws governing the natural world to the social conventions governing interactions between humans. Concerning social systems, it is easy to see that they are made up of heterogeneous elements, whose conditions are highly dependent on the conditions of others. Therefore, they can be seen as complex environments, where interactions can bring about global dynamics that are more than the sum of individual behaviours.
So far I have considered examples from different spheres. Henceforth I limit the discussion to cases which might be of interest to the social sciences to see why and how complexity came to be considered by policy makers. I briefly discuss how complexity arises in business contexts and then I move on to consider the case of the public sphere in greater detail. In general, I pay greater attention to language-related cases.
1.5.1 Complexity in business and management
The issue of complexity has largely been explored by scholars in business and management since the early 1990s, especially from an organizational be-haviour perspective (see, for example, Simon 1962; Stacey 1996; Levinthal 1997; Anderson1999; Kelly and Allison1999; McKelvey1999; Chiles, Meyer, and Hench 2004; Gruhn and Laue 2006; Gharajedaghi 2011; Straub 2013).
Complexity theory is of particular interest for strategy choices in large corpo-rations characterized by interconnected structures. However, some authors noted that complexity theory is still struggling to become a fully accepted approach to business management. Straub (2013) suggests that this is in part due to the reluctance of managers to accept a complexity approach in that it would inevitably make their job dramatically harder.
Traditionally, corporate strategy has been treated as a centralized activity, under the responsibility of corporate executives who are often supposed to be in possession of all critical information and to have the right incentives to make decisions for the good of the company as a whole (Eisenhardt and Piezunka, 2011). Besides, many large companies have a business-unit (BU) structure, whereby BU-executives have information limited to their own BU
and make choices accordingly (Hill, Hitt, and Hoskisson, 1992). This defi-nition reflects an approach to business management as a complicated issue, as opposed to a complex one, whereby an optimal strategy of local units is expected to lead to optimality for the company as a whole. Concerning com-plex strategies in multinational corporations, Eisenhardt and Piezunka (2011) (quoting Chandler 1962) propose the telling example of the large chemi-cal company DuPont, which turned from a single-business firm into a large multinational company operating in several markets in the post-War period.
Initially, the company retained its strongly centralized organisation, which led to poor performance. As a reaction, executives restructured the company into several loosely-linked units, which resulted in a significant performance boost. As another example, they also mention the case of General Motors, which was initially a group of several independent producers. Only when the company started organising these units in such a way as to create links among them did the company reach high levels of performance.
Multinational companies (or, better, companies dealing with people from different cultural backgrounds, which are not necessarily multinational)25 of-ten find themselves in the situation of having to decide on language issues for internal and external purposes. As is discussed more at length in Chapter 2, a constantly recurring issue is the trade-off between prioritizing the use of a single language and continuously adapting to the local context, especially when managing the aftermath of a merger or acquisition. On the one side, unrestricted multilingualism can cause severe inefficiencies. On the other side, though a common language could boost cross-border collaboration, it can come at a very high price, such as, shadowing talented workers who are not proficient in it. Besides, it can (quite paradoxically) generate misun-derstandings due to non-proficient use. Neeley and Kaplan (2014) mention the detrimental promotion of a Japanese worker in a US company’s Japanese subsidiary exclusively based on his fluency in English. Only later did it ap-pear clear that he was not the best performer and most deserving employee, and that his promotion, based on a severely biased view, had generated dis-content among his peers. Besides, a common language does not amount to a common culture and definitely not to a common set of values and underly-ing assumptions when it comes to handlunderly-ing interpersonal relationships. Re-searchers call “cultural clash” the phenomenon of disruptive tension expe-rienced by individuals when they have to interact in a second language and
25This is the case of many national companies working in multilingual countries, though the “one-nation-one-state-one-language” ideology is still somewhat resistant.
adopt the ways of another culture (Berry,1983). Many scholars found that in-tercultural interactions are very likely to generate misunderstandings, in that individuals are used to (sometimes radically) different social customs and rules (Calori, Lubatkin, and Very, 1994; Weber, Shenkar, and Raveh, 1996;
Besides, language issues develop in different ways at different levels. In fact, micro-linguistic behaviours can differ significantly from those at the macro level. Even if we conceived language choices and behaviours sim-ply as the strategy that guarantees optimality of communication overall, it is easy to see that accommodating one’s own individual needs is very different from meeting the need of a large (and possibly linguistically and culturally diverse) community. Besides, discussing the scale of complex phenomena, some authors found it suitable to mention also a meso-level (as defined in the previous pages), recognizing that the micro-macro dichotomy is better described as a continuum rather than two separate points (see, for example, Kaplan and Baldauf1997).
1.5.2 Complexity in public policy
Most policy decisions more and more frequently concern complex systems.
Their effects spread all over the system concerned through multiple actions and reactions (Bankes,2011). For example, environmental policies are never strictly environmental, in that they are introduced in a system that has sev-eral other characteristics (political, cultural, demographic, economic. . . ). This is a rising trend. Government officials and policy makers deal with very di-verse systems involving a large number of interacting parts (OECD, 2009).
Therefore, complexity science constitutes a valuable approach to policy mak-ing and policy evaluation, especially once we recognize that the questions the policy is trying to answer as well as their effects are better described in probabilistic rather than deterministic terms, in order to provide room for randomness. Nevertheless, the fact that the object of interest is complex does not amount to saying that a complex approach is being applied. As a matter of fact, complexity theory is only seldom applied to policy making. Indeed, evidence-based policy making often assumes linearity and stability (Room, 2011) or, in the words of Friedman (2005), a flat world, in which globalisa-tion processes have contributed to the homogenizaglobalisa-tion of behaviours. On
the contrary, one might as well argue that globalisation has most likely in-creased complexity, in that it has drastically reduced global “viscosity”,26 to put it in physical terms. Changes in today’s world are much more likely to reverberate over a way wider area than where they have strictly taken place.
Examples of applications of complexity theory in the public domain are numerous. When studying the patterns of contagion of certain epidemic ill-nesses, it is often assumed that a population is homogeneous, i.e. each indi-vidual has the same probability of being infected (OECD,2009). This some-what unrealistic assumption can be easily overcome by applying the tools of complexity theory, allowing to take into consideration heterogeneous popu-lations and to make more reliable predictions. Another example concerns the management of traffic networks. Recognizing that predictions were not al-lowing for enough flexibility to account for every possible human interaction, complexity theory was applied to make more accurate transport predictions (Marshall,2004; Avineri,2010). Advanced complexity-based modelling tak-ing human cognition into consideration has been used also to predict human traffic behaviour, in order to avoid stampedes and to locate emergency exits during highly crowded events (Bonabeau,2002).
Complexity theory provides a new way of looking at policy matters, where dynamic (rather than static) effects play a major role. How does the complex approach differ from the traditional tools of policy making and policy eval-uation? First, complex systems science re-orient the focus of policy makers.
While traditional policy making is centered on making accurate predictions of events, a complex approach puts more emphasis on trends and probabili-ties. In other words, as said before, it introduces an element of randomness when modelling phenomena. In algebraic terms, it switches from a generic formy =mxtoy= mx+e, where eis a term capturing random and unpre-dictable events. Second, the complex approach recognizes that cause-and-effect chains cannot be thought of as independent, parallel processes. Rather, they should be conceived as a tightly meshed network where causes and ef-fects are intertwined. Finally, the complex approach abandons the safety of a strictly quantitative understanding and shifts the attention of policy mak-ers towards the underlying patterns and mechanisms that make certain phe-nomena complex. The complex approach recognizes that it makes no sense to look for a one-to-one cause-and-effect relation. It studies behaviours and re-veals the subtle links connecting the world in sometimes non-obvious ways.
26Viscosity of a fluid is the property that describes its resistance to deformation. Simply put, a less viscous liquid is one in which it is easier to swim, such as water, as opposed to one that poses much more resistance, such as honey.