Dare an ever more complex world to subdue with institutional evolution

To deal with the complex world phenomenon, we need more ideas which implicitly emphasizes the importance of more entrepreneurial economics. In such economics a creative world is assumed as a continuum system that is to adapt to unforeseeable changes by representing new opportunities and solutions in the adjacent possible. Entrepreneurial economics includes a process of discovery of how economic patterns change under the influence of technological innovation as well as processes of economic design to shape economic patterns. In such a context, it appears to be important to identify, observe, question and discuss economic patterns in the sense of patterns observable in the real world and in the sense of patterns of economic thought to allow our shared mental models to appreciate and adapt to a world with artificial intelligence.

Contemporary AI is at the beginning of a dynamic journey from narrow to more general intelligence. The natural habitat of AI agents is environments with accessible digital data. This may be the so-called “big data” environments which can be defined as “the information assets characterized by such a high volume, velocity and variety to require specific technology and analytical methods for its transformation into value” (Mauro et al. 2015).

Human–Agent Collectives or HAC is where AI agents get engaged with humans to operate effectively and sometime autonomously actions. In this engagement process the humans and the agents switch to take the lead and this relationship can vary dynamically (ibid: 80). More generally, HAC arise within and in between already existing institutions like states, markets, communities, firms, non-profit organizations, governmental and non-governmental organizations and more. North (1994) argues that “institutions matter”. Institutions influence human beliefs and actions and thus have an impact on social, political and economic outcomes. AI is already here, and it will give a special twist to these institutional matters. A close look into existing traces of economic analysis can offer some foreseeable economic patterns (Wagner 2020):

1. From homo economicus to machina economica.

2. Micro-division of labour

3. Triangular agency relationships and next level information asymmetries.

4. New factors of production.

5. Economics of AI networks.

Let’s begin with a general homo economicus view that social outcomes arise from the interactions of utility-maximizing human individuals who make more or less rational decisions (Kirchgässner 2013). However, this idea has received criticism and more flexible perspectives have been proposed (Tomizuka 2015). The influence of neoclassical economics on management science (Ghosal 2005) and the further development of business administration and organization science have highlighted the role of organization in economics.

Now AI and AI agents enter economics and as economic actors. They behave algorithmic rather than fuzzy, it acts always dispassionate rather than sometimes emotional, and its reasoning is logical rather than intuitive. But whilst the new species of “machina economicus” (Parkes and Wellman 2015) or rather “machina economica” behaves more economic than humans, it too is faced with bounded rationality. Algorithms work with finite computational resources which in practice means that they cannot achieve Turing completeness and are limited to linear bounded automation (Hopcroft et al. 2014).

Thanks to Dirk Nicolas Wagner “Economic patterns in a world with artificial intelligence” 2020.