Conclusions

We are aware that we have addressed a very broad issue in this study, and also that we haven't provided strong evidence for the hypotheses we threw into this work. But we think that this is still time for opening new frontiers of thinking on cognitive governance, more than testing precise models of how knowledge societies work.

Digital society is not a frictionless society; it is a very complex system. Network technologies haven't eliminated transaction costs; they have increased interactions and therefore complexity. Complexity creates more and new transaction costs, and therefore new cognitive hierarchies, even though it also creates new opportunities for self-organized and distributed models of coordinating interdependencies.

The central idea of this work is that we must understand the new hierarchies of digital societies and digital economies, instead of following simplistic visions of a "technologically-determined-hierarchy-free" digital future or dismissing the problem, assuming that digital societies will be "business as usual."

In order to achieve this goal, at the economic level, we propose to consider businesses as complex evolutionary value networks. Differently from previous "value-network" theoretical frameworks, in our idea:

1. The value network is not made by nodes all at the same level. First there are asymmetric/power relationships at the dyadic level, due at least partially to the different stocks of resources available at the node level (Mandelli, 2004); second the network is made by both elementary and second-level nodes. Second-level nodes work as infomediaries, conceptualized as cognitive intermediaries of access to information and social relations. These are the new cognitive hierarchies we have introduced in the previous chapter;

2. Nodes are conceived as cognitive, socially constructed, maps; they can be individual or organizational level, implicit or codified. Also networks are cognitive structure, socially constructed;

3. The business value network in this approach is not driven only by customer value creation goals, but also by node value creation goals, meaning that all participating nodes must perceive that in the long-term they can extract value from network membership;

4. Node activation can be intentional or unintentional. Intentional activation is driven by perceived value extractable or imitation. Value extractable is evaluated on the basis of learned rules (lessons from the past) or calculations (short-term or in the shadow of the future). Unintentional activation can be inertial or random;

5. Mediations always create value and consume value, but the value created and extracted is not always evaluated in order to take a calculated choice. If we define as economic the exchanges which are based on evaluations of the net value extracted, we can say that not all the value created in a value network becomes economic value, even though the stock of cognitive and social capital influences economic exchanges;

6. Network structure, and therefore value creation and value capture, are not only bounded by economies of relationships, but also by economies of content and infomediation at the network level;

7. Since nodes are socially constructed cognitive maps, they evolve adaptively through interaction and learning; the network itself is a socially constructed dynamic cognitive representation.

The conceptual framework of cognitive evolutionary value networks can help managers design more cooperative structures and nodes to negotiate network structure and power. The dynamic negotiation of the network changes the distribution of power and wealth in the network. The design of the network matters. Value exchanges cannot be easily programmed, but value exchanges can be evaluated ex-post. The network is the locus of learning, but also the locus of social negotiation.

The new institutions are cognitive infomediation structures, evolutionarily emergent from the interaction between the economies of complex networks, the self-organized economical and cultural exchanges at the node level and collaborative/negotiated policy choices at the network level. Trust plays a major role in the formation of these new institutions because it works as resource and as hierarchy. Its dynamics of delegation rests at the basis of the new infomediation structures and institutions. From a policy standpoint, trust and social capital are both dependent and independent variables. They set the constraints and social trade-offs of governance structures. But they are also the object of investments and decisions. The new institutions can be designed, even if this design looks more like a monitorial learning process and a trial-and-error course of action than the well-structured plans of old-fashioned management.

Future works, in the business management area, should concentrate on a better understanding of the difference between the governance of relationships between firms and consumers, the inter-firm relationship governance and the intra-firm hierarchies. They also should build testable hypotheses and provide stronger empirical evidence for these tests.

But there is also much to understand about the social consequences of these changes. With Powell (2001) and Rullani (1998), we call for a renewed critical research effort in studying the social and ethical impact of these transformations in society. Flexible and dynamically built networks don't necessarily increase freedom and cooperation in society. We must understand better the effect of radical flexibility on the quality of human life and societies.

The logic that wants to build organizations as flexible and loosely-coupled systems of self-organized peripheries, applies to organizations, the logics and ethics of biology. But in biology we do not feel sorry for the collapse and abandonment of unsuccessful options. Instead, in social organizations evolution is history, variation is made by people and organizations of people, and unsuccessful options may be human tragedies. We have to deal with the management of failures and the negotiation of the failure consequences, beyond the frictionless ideology. In a trial-and-error paradigm, it can turn out that it is frictionless only the right for the network elites to place all the emergent benefits at the system level and all the emergent costs of trying at the dispersed node level.