A New Institutional Economist Approach
This post is the first in a series of posts I dedicate to the review of an interesting book by the Nobel Prize laureate, Douglass Cecil North: Understanding the Process of Economic Change.
The entire review is now available on Dianoetic.
As the title suggests, the book studies the mechanism of economic change. Douglass North is a New Institutional economist, and thus, he has a conspicuous emphasis on the role of institutions and social structures on the economy and its evolution. In fact, he identifies three important factors as the key parameters determining the process of economic change: demographic characteristics, the stock of knowledge, and institutional change.
The main message of the book in North’s words is:
“You have to understand the process of economic growth before you can improve performance and then you must have an intimate understanding of the individual characteristics of that society before you are ready to try to change it. Then you must have an understanding of the intricacies of institutional change to be effective in undertaking that change.” (p. 165)
In the rest of this post, I will present North’s ideas on the role of learning and knowledge in the process of economic change. I will explore the other key parameters, i.e. demographic characteristics and institutional change, in future posts.
Throughout the book, North repeatedly emphasises on the role of learning in advancing economic change. As I previously mentioned, the stock of knowledge is one of the three main factors that North identifies as the determinants of economic evolution.
The importance of the stock of knowledge stems from the effect of uncertainty on human activities and interactions. Our confrontation with new situations and novel phenomena can be one the two general categories:
- Uncertainty: It is when we can have a probability distribution of the outcome of the situation.
- Ambiguity: When we cannot come up with any probability distribution for that situation.
To confront and reduce uncertainty, we impose structures to our social, political, and economic interactions. However, we cannot eliminate uncertainty because
- Our understanding of the reality is imperfect.
- Our formal rules and their informal enforcement mechanisms are imperfect.
An important feature of the human world is its nonergodic nature, in contrast to ergodic structures which are constant and thus, timeless. This feature is one of the main reasons why we are unable to understand the “reality” of political-economic systems. However, this lack of perfect knowledge does not prevent us from constructing belief systems about reality, which are both positive in describing the system and normative in prescribing how it should be.
The reaction of agents to novel situations depends on the novelty of the problem and the cultural heritage which can have solutions to that specific problem or those similar to it. Here is where the concept of learning comes into play.
“Learning entails developing a structure by which to interpret the various signals received by the senses. The initial architecture of the structure is genetic, but the subsequent scaffolding is a result of the experiences of the individual—experiences coming from the physical environment and from the socio-cultural linguistic environment. … Building on these classifications, we form mental models to explain and interpret the environment … the mental models may be continually redefined with new experiences, including contacts with others’ ideas.” (p. 25)
The human brain works based on pattern recognition rather than abstract logical reasoning. Thus, we usually learn, and our cultures adopt new ideas if those ideas have some similarities and share some features with our norms. That is why the connectionist approach to machine learning seems closer to the way our neural networks work. Connectionist models are statistically driven and learn through examples. The approach is in contrast to artificial intelligence approach, in which learning is through symbols and symbolic manipulation.
What a generation of human beings learns through its lifespan can be transferred to the next generation through the cultural heritage. Culture accumulates partial solutions to problems encountered in the past. While it transfers the learnings of the past, it partly conditions the learnings of the future.
Two main factors that influence learning process are
- The experiences in confronting the world
- The belief system and the way it filters information from those experiences
The cultural heritage creates the artefactual structure, which includes beliefs, institutions, tools, instruments, and technology. A rich and favourable artefactual structure reduces uncertainty in decisions making and helps the individuals and the society overcome novel difficulties. In other words, the richer the artefactual structure, more successful the society.
“Human cognition is not just influenced by culture and society, but that it is in a very fundamental sense a cultural and social process” (p. 34)
Effectively combining distributed knowledge requires an effective price system as well as institutions and organisations to handle essential public goods, asymmetric information, and ubiquitous externalities.
 This categorization is somehow reminiscent of Sheila Dow’s notion of Babylonian and Cartesian/Euclidean modes of thought and their approach to uncertainty. In Babylonian thought, it is impossible to acquire full knowledge. Thus, information cannot be categorised into known or unknown. In Cartesian/Euclidean thought, knowledge is treated dualistically. It acknowledges risk in the sense that knowledge might be subject to the probability distribution. That distribution, however, is either known or unknown. This mode of thought excludes uncertain knowledge from theoretical systems (Dow 2012: 60).
Dow, SC (2012), Foundations for new economic thinking: a collection of essays, London, Palgrave Macmillan UK.
North, DC (2005), Understanding the process of economic change, New Jersey, Princeton University Press.