Austrian Economics Is On The Right Side.

/0 Comments/in , /by

Iain McGilchrist is a neuroscientist, psychiatrist and philosopher who devoted his research work to illuminating the proposition of hemisphere differences in brain function. We all have a left brain hemisphere and a right brain hemisphere, and they function (or “see the world”) differently. McGilchrist makes long lists of the differences in worldview attributable to the hemispheres.  We can selectively highlight a few here:

The left hemisphere (LH) deals with detail, the local, what’s in the foreground, and easily grasped. The right hemisphere (RH) deals with the whole picture, including the periphery and the background. this local versus global distinction is one of the major differences in the processing of the two hemispheres.

  • The RH is on the lookout for and better st detecting and dealing with what is new. The LH deals with what is familiar.
  • The LH aims to narrow things down to a certainty, while the RH opens them up into possibility. The RH is comfortable with ambiguity and holding pieces of information that appear to have contrary implications, whereas the LH makes an either/or decision in favor of one of them.
  • The LH’s world tends towards fixity and stasis, that of the RH towards change and flow.
  • The RH recognizes uniqueness and individuality. The LH tends towards more generalization.
  • The RH is essential for empathy, and emotional receptivity and expressivity are greater in the RH.

McGilchrist’s thesis is that the left hemisphere and its mode of thinking has become dominant in today’s world, and that dominance is a disaster for civilization. Why? What he calls the left hemisphere world 

  • has lost the broader picture
  • favors data over knowledge
  • has lost the concepts of skill and judgment
  • favors bureaucracy (procedures that are known and predictable)
  • elevates quantity as the only criterion versus quality
  • dismisses common sense
  • discards tacit knowledge
  • has a need for total control
  • has more anger and aggression
  • loses social cohesion
  • is characterized by passive victimhood

In the field of economics, we can clearly see McGilchrist’s left hemisphere versus right hemisphere dominance in action. Mainstream economics, the style that is practiced by government and the Federal Reserve, taught in academia and written about in the New York Times, exhibits a left-hemisphere dominated pattern. Austrian economics is more right-hemisphere, in stark contrast.

mainstream economicsAustrian economics
Principally concerned with mathematicization, modeling, aggregates and related variables (x causes y).Principally concerned with the economic system and its emergence as a result of the purposeful actions of individuals reasoning subjectively.
Empirical, working with data series and tables and numerical outcomes.Verbal and logical, working with language and reason, observing behavior to deduce motivation and purpose.
Captures data in re-usable mathematical symbols and algebraic formulae. Seeks to construct reusable/repeatable models.Deals with uncertainty, dynamics, constant flux, and flow.
Equations are solved, models are completed and self-sufficient. Point predictions.Descriptive and not predictive. Assumes constant change, uncertainty and non-linearity.
Positivist, adopting the methods of physical sciences.Humanist, adhering to the approach of social sciences.
The economy as a machine to be tuned.The economy as a complex adaptive system. Interaction of many components. Emergence.

The focus of Austrian economics on real people, individuals interacting in the pursuit of subjectively-assessed value, creating a dynamic flow of activity of benefit for all contrasts starkly with the mainstream economics focus on mathematical models and solving equations, aggregate quantities like GDP, and unremitting regulation and government intervention aimed at control.

Until we rebuild our institutions from a more balanced perspective, releasing the hold of the left hemisphere on our thinking and behavior, we are condemned to follow the downward spiral into a command-and-control economy.

This Is Value Entrepreneurship – The Business Method Fueled By Entrepreneurial Economics.

/0 Comments/in , /by

Entrepreneurship is the business driver – of revenue and growth, of the customer base and customer loyalty, of innovation, of cost reduction, of everything about business that constitutes success. It’s true of businesses of every scale – every firm must be entrepreneurial to succeed.

Value is the purpose of entrepreneurship. On the Mises Institute Economics For Business (E4B) website you’ll learn deep insights about value – that it’s not a thing but a feeling, that it’s the outcome of a learning process, that you can’t put a price on it, but people will pay for an expectation of value. There’s a lot to learn about value.

Combining the two in Value Entrepreneurship provides you with an understanding and a toolset to pursue new value for customers at every scale, in every firm, via every project, process and job. Value Entrepreneurship is the business system fueled by entrepreneurial economics.

Let’s first examine and prepare for entrepreneurship. Entrepreneurship is action. While MBA programs may focus on strategy and planning and finance, E4B’s alternative approach emphasizes action. Entrepreneurial action can be broken down into two components – the decision to act and the action itself.

The decision is a hypothesis. There is more uncertainty in business than can ever be resolved. You are never certain. The most you can expect is to narrow down your choices of possible actions to a small number. You develop a hypothesis of what could work based on two inputs. First, an analysis of whatever information or data is available to you that tells you something about prevailing market conditions and constraints. And second, the synthesis of your data-based conclusions with your instinct and intuition, your assessment of dynamics and what might change in the future, as well as your creativity and ideas. From analysis and synthesis, you generate hypotheses of all the things you could do that are aligned with your intent, and choose as many of those as you’re capable of implementing – that’s your capacity, which might be governed by available funding, staffing or capital goods such as your AWS service agreement.

With the decision made, you act. Decisions are hypotheses and actions are experiments. The purpose of an experiment is to generate learning. Find out what works and what doesn’t, so that you can do more of what works and abandon what doesn’t. If you run as many experiments as possible, the fittest business strategy will emerge. Complex systems theory refers to this process as explore and expand. That’s what entrepreneurship consists of: exploration followed by expansion.

We learn because action generates interaction – with customers, retailers, markets, competition, media, and the entire business ecosystem. Interaction, in turn, generates a feedback loop. Customers buy or don’t buy. They enjoy their experience, or they don’t. Or, most likely, they partially enjoy it but there are some drawbacks that the entrepreneurial business can respond to and rectify if they can properly gather the right knowledge.

That brings us to the second part of Value Entrepreneurship – the value part. We just referred to the customer’s experience. That’s what value is – an experience, subjectively felt and evaluated by customers. Value is formed and experienced entirely in the customer’s domain. As you’ll appreciate as you enter more deeply into this way of thinking, the customer is the driver of your business. Customers are the sole determinants of business success or failure. They determine what gets produced by buying or not buying – by not buying, they ensure that production stops and business resources are redeployed to new uses. 

Customers are always evaluating, and thereby producing value. They do this from the context of their own system. Let’s take an example of a consumer household and its systems (although we must emphasize that the value entrepreneurship model applies equally to the world of B2B, not just B2C). Let’s take one sub-system: food and nutrition for the family. There’s a system of deciding what to eat and drink, there’s a system of shopping, whether online or offline or both, there’s a system of storage, perhaps involving freezing, refrigeration, and room temperature. There’s a system of preparation and cooking, involving a lot of home appliances. There’s a system of cutlery and place settings, and another for washing these. Taken altogether, it’s a complex system. And it may be continuously changing. What if the family Is becoming more conscious about healthy eating? What if they start substituting lower-calorie foods for higher-calorie versions? What if they start reading ingredient labels? What if they buy more fresh food and less manufactured food? What if they discover new preparations like blenders? 

We can see the physical manifestations of these changing experiences in the market. The periphery of the supermarket where the fresh foods are sold becomes bigger and the center contracts. Healthy cookbooks appear on amazon and social media. Fresh fruit appears in more convenient packaging and new varieties flourish. New brands of healthier crackers and desserts abound.

The point about value is that it is formed in the customer’s system, that system is complex, and it’s always changing. The role of the value entrepreneur is to observe the system, understand the system, fit into this system and make a contribution. It’s possible to identify gaps, maybe gaps the customer is not even aware of. Most importantly, there’s the potential to identify the system the customer will prefer and move to in the future, ideally before they get there themselves. This is value innovation – imagining and inventing the future. Whether in the present or the future, the entrepreneur’s contribution is to help the customer to feel satisfied that they’re making the best choices within their own system. Their system is life, and entrepreneurs help make the system work for them.

Entrepreneurs and entrepreneurial businesses facilitate this feeling of value – make it possible, make it robust, make it repeatable. They are rewarded by customer purchases, and value flows back to the firm as cash flow, to be reinvested in more production and more innovation. The value entrepreneurship loop is continuous. 

The Long Night Of Mathematicized Economics Is Over.

/0 Comments/in , /by

Sometime in the 19th century, economists got the idea that mathematics was an appropriate language for economics. This was a strange turn, since economics is the science of how human beings, pursuing their own desires and preferences, find ways to exchange with other human beings to produce prosperity. Finding ways to exchange involves creativity, ideation, innovation, forming companies, providing services, importing and exporting – activities and processes that don’t lend themselves to algebraic symbols and mathematical equations. 

Mathematics is inappropriate for economics, yet it has become dominant. Why? Precisely because it removed the human component, the creativity, the subjectivism. The new practitioners didn’t want that mode. They wanted cold, hard calculation. They wanted to be seen as “real scientists”, like physicists and engineers, dealing in scientific precision and not the uncertainty and softness that, in their view, characterized the analytics of social science and social interaction. Human-ness is so messy. Math is clean and sterile.

But the hold of mathematics on economics is breaking. Its inappropriateness as a tool of economic understanding is widely recognized, and new alternatives are becoming well established.

The Illusion Of Knowledge

In an essay entitled The Illusion Of Knowledge, Howard Marks, co-founder of Oaktree Capital Management, points out that the mathematical models of economics can’t predict and can’t guide expectations. There are too many variables and the most important variables are human and therefore unpredictable in their reaction to changing circumstances. He asks

Can a model replicate reality? Can it describe the millions of participants and their interactions? Are the processes it pretends to model dependable? Can the processes be reduced to mathematics? Can mathematics capture the qualitative nuances of people and their behavior? Can a model anticipate changes in consumer preferences, changes in the behavior of business, and participants’ reaction to innovation? In other words, can we trust his output?

Howard Marks, The Illusion Of Knowledge

His answer is no. 

Economics Of Verbs Not Nouns

W. Brian Arthur is another prestigious economist who rejects mathematical modeling for his science. In a paper entitled Economics In Nouns And Verbs, he observes:

The economy is very much a creation of humans and a very complicated one, so given the liberty of human choices and the vagaries of people’s actions, it is not obvious why algebraic logic and calculus should apply.

W. Brian Arthur Economics In Nouns And Verbs.

The core problem, as he articulates it, is that the algebraic symbols of econometric models exclusively represent nouns:

Economics deals with prices, quantities produced, consumption, rates of interest, rates of exchange, rates of inflation, unemployment levels, trade surpluses, GDP, financial assets, Gini coefficients. These are all nouns. In fact, they are all quantifiable nouns— amounts of things, levels of things, rates of things. Economics as it is formally expressed is about amounts and levels and rates, and little else.

Ibid.

The language of mathematics does not allow actions. It can’t answer questions about how an economy or a firm or a development project emerges in the first place, how innovation works, how economic development takes place, or how structural change happens. Anything in the economy that deals with adjustment— whether it is adaptation, innovation, structural change, or history itself—falls through the algebraic mathematics sieve.

Arthur calls for a language of expression in economics that can describe these actions, changes, and innovations, and thereby develop some resemblance to and understanding of the real world.

Language and Austrian economics

Ahmed Elsamadisi, CEO of the analytical software company narrator.ai says that there is one data model that can cope with all these variables: it’s called language. Data has a role to play, but it’s subsidiary to language. Elsamadisi talks about “having a conversation with data” – it’s human beings using language to ask the right questions who are able to find the most value in historical information.

The economics that Brian Arthur is looking for already exists, but most economists ignore it. The unfortunately-named Austrian economics – its first scholars were in Vienna but the research now is global – uses language and deductive logic from first principles to develop its theories. It has no models. 

As economist Per Bylund writes in How To Think About the Economy, economics Is about people and specifically about what they do to meet their own goals. People’s wants are unlimited but their means are not, so they economize, choosing from scarce means to achieve as much as they can. Economics is life: spontaneously and adaptively doing what we can with the resources available to us. As a consequence, the economy Is unplanned order, always in flux. It can’t be modeled.

Subjective Quantification

Professors Peter Lewin and Nicolas Cachanosky propose a novel combination of thought processes to integrate words and numbers in economic calculation. Their term is subjective quantification. Human beings have the unique ability to turn subjective ideas into numbers that express the idea. We find a particular product or service that we feel has potential value to us, and we turn that feeling into a number via our willingness to pay a monetary price to acquire it. That’s turning the subjective – our idea of value – into the objective – the number of dollars we’re willing to pay. Generally, this is the process of economic calculation. Firms estimate revenues they may receive in the future from customers who perform this value calculus. The firm uses its own estimate – a subjective one, by definition – to make plans for investment and business expenditures.

Models promise prediction and guidance on economic variables. It’s an illusion, as Howard Marks puts it.

With the new resurgence of Austrian economics, and its modern expression in what Brian Arthur calls complexity economics (an economics of verbs not nouns), the dominance of mathematics in economics is coming to an end.

No-One Understands How Systems Work.

/0 Comments/in , /by

You may have been listening to economists debating and arguing about the state of the economy and the future of inflation. Are we in a recession or not? What will the economy look like next year? What causes inflation? Will the rate of inflation increase or decrease? What can be done to alter the future direction of change?

There is no shortage of opinions, but a total lack of certainty, of confidence, or even of sound theory.

You probably find the same phenomenon when engaged in discussions about business – about how firms can do a better job of creating customer value, to grow and succeed. How can they achieve a rise in stock price? What are the most critical constraints? What’s the best process for driving innovation? What’s the best way to manage and incentivize employees and to build a strong culture? Those are the kinds of words and phrases business consultants and business school professors use – your own peer conversations are probably more to do with increasing sales, or lead generation or your P&L, or whether all the ingredients you need will be delivered. 

But the challenge, in all these cases is the same. No-one knows anymore how these systems actually work. We can extend the list to climate as a system – some scientists claim they know how it works and can forecast the future, and another set views it as unpredictable and unmanageable. 

We saw during the so-called pandemic that virologists and infectious disease experts and pandemic modelers got their predictions – and their policies – hopelessly wrong, and it cost millions of lives and billions if not trillions of lost economic production. We can see the might modeler Ph.D.’s at the Federal Reserve make the same hubristic mistakes with their models of money supply, inflation, employment, and economic growth. The only thing they’ve ever been is wrong.

In fact, there’s a whole new science of not understanding systems which is called complexity theory, which overlaps closely with chaos theory. It says the following: A system is a collection of elements, components, parts, pieces, or, generally, agents. In an economic system, the agents might include individuals, families, and firms. There are additional elements like processes, government and institutions, norms, traditions, and a whole lot more. These elements and agents interact with each other and with inputs and outputs. There is further interaction after feedback loops establish themselves – agents react to outcomes and results they didn’t expect or anticipate. There are so many variables – individual decisions and preferences, group behaviors, trends, technological changes, money supply changes, etc, etc – that the interaction is described as complex, which means beyond understanding, unpredictable, and non-linear (it goes off in directions and at speeds that no-one expects).

Complexity is science throwing up its hands and saying, we don’t understand these systems anymore, can’t predict them, can’t control them, and can’t manage them. We don’t even have any ideas on how to do so.

There are many fancy new words that come out of this science. One, for example, is emergence. Systems have emergent properties, meaning s*** happens that we can’t account for and couldn’t even imagine in advance. Emergence is a kind of magic.

Another new term is self-organization, which means that the system will evolve and develop as it likes without any input -or despite any input – from the scientists.

Don’t worry, there are lots of government grants being given for the study of complexity, lots of papers and journals, lots of conferences, and lots of sabbaticals being taken to contemplate future studies and grant applications. There is money in complexity.

What’s the alternative? Pragmatism.

When the system (of life, of business, of health, of the economy) is complex to the point of incomprehension and unpredictability, there is only one action: do something and see if it works or it doesn’t work. In business, it takes the form of what is called today A/B testing. Try two different actions (without any prediction or bias or even desire as to which outcome will result) and choose one that works, i.e. moves in the direction you feel is better. Then do another test and another and another until there’s a string of results. Expand the actions so long as they keep working. Start a small store and keep expanding it unit by unit until there’s a big store. Be prepared for things to change without notice. Go back to square one if they do. 

There are fancy terms for this too. Economists call it entrepreneurship. Constantly trying and re-trying, combining and recombining, testing and re-testing. If a promising pattern is established, pursue it and reproduce it, but only so long as the pattern holds. Drop it as soon as it becomes erratic. Start another one. Creativity is the required skill set, and the whole point about creativity is unpredictability. 

Creative entrepreneurship doesn’t try to study complexity. Entrepreneurs face it every day and they take action rather than studying it.