Economic theory of value, price and utility

Price is a very sensitive topic in microeconomics. There has been a lot of work done in this domain. Unfortunately most of it is completely misguided by attempts to theorize about price from a normative thinking process of ‘what should be the price?’ or ‘how should a rational individual think about price?’

While going through a lecture on Coursera on Neuroeconomics, I revisited this question again in my head. Partly it was prompted by the idea in the lecture itself – that the ‘value’ something has for an individual has been generally expressed in terms of price or utility in economic theorizing and that the Neuroeconomic approach to it is to start with neuronal firing rate in response to a good/activity and its like reward or punishment value. Interesting and promising approach indeed.

What i was tangentially thinking about was something related but different. I was wondering if the entire framework of answering the question ‘what is something worth to an individual?’ is mistaken. Here’s why.

The conventional attempts at finding out worth of something to someone is generally focused on the utility of the good/service to the person and some estimate of the value of the same. There is an implicit assumption that this is constant across space and time and individuals. All three are faulty assumptions – there are not even good first level approximations. That is the reason behind my question above.

Part of the reason is the dependence of value on location, time and individual. Part is also the very approach of trying to model it like this. When one implicitly assumes that something has an objective value and that just needs to be determined through some observation, one is already committing the folly of creating an imaginary quantity (objective value). One is then likely to fall into the traps of calling something over-valued, under-valued, over-priced and so on.

My view on value and price is as follows.

Value of something is inherently and fundamentally subjective as well as a function of time and place. It is also reflexively dependent on perception, network effects and inference about who else is a consumer.

First of this is simple to demonstrate.

1. Dependence on person: I like QWERTY keyboard phones while someone else values bigger screen. Even at an aggregate product level, someone may like orange juice as a refresher while her friend might prefer a quick call with her fiancé. Given a specific good, different people will value it very differently – I love tea, my wife does not have tea at all and i know of a lot of people who have intermediate levels of liking for it.
2. Dependence on time: Food is valuable when hunger strikes, music adds value to a pub night, cab service is more valuable in monsoon and wee hours etc etc
3. Dependence on place: Mineral water at the top of a mountain is more valuable than in the middle of the city, binoculars are of value in a desert but not as much in a jungle and so on.

Note that if we start without the baggage of goods having a similar price across people, time and place, the above divergence would point us in the direction of a non-unique value and price automatically. Only if we start with the state of the world as it is today that we would attempt to figure out explanations and workarounds to this obvious state of affairs.

Second set of divergences is more subtle. It is also more applicable to modern branded goods than to commodities.

1. Dependence on network effects: An app that my friends use is more valuable than one that nobody uses.
2. Dependence on perception: If Hyundai cars are not perceived to be premium, I would flinch in buying a feature-rich Hyundai car for a high price point.
3. Dependence on inference about who else is a consumer: If everyone is using Ray-Ban shades, I would also join in. Sometimes this also has adverse effect – if everyone (‘the masses’) is using Gucci, i better stop using it (it has become ‘pedestrian’)

The value derived from a good/service is hence a complex function of all of the above factors. Just to be clear, these are not factors that are small deviations around a secular level. This is precisely the mistaken stance conventional microeconomics takes. Most economists would acknowledge the presence of these deviations. However, in the name of tractability and approximations, they would make an undefendable leap of faith that a large proportion of value is independent of this and thus can be thought of as objective.

The other angle often ignored in classical economic theory with regard to price is how the producers determine it. Most often, the over-generalized response of economists is that perfect competition persists in most cases and the producers charge a price that is equal to marginal cost of production. This is again normative. It is demonstrated in real life only in a small minority of cases where a highly uniform commodity is traded in a highly transparent manner (crude oil, steel etc). For most real life cases, price is nearly arbitrarily determined – producers do take into account the cost plus logic but more often than not, the linkage is reflexive. If something fetches good price, its input goods start to reflect that as well through higher pull. It is not only that input good become costlier and thus output good catch up in terms of price.

Just like we don’t know how each specific biological species began its journey on earth, the pricing history of each specific good and service is hard to trace back. Since everything has something or other as input (including labor) which has its own price, it is hard to study the absolute level of price of anything in isolation. However, that should not make us complacent about the origin of price-levels.

A feedback loop exists between consumers and producers as well and it is not simply a matter of a producer looking to offer at a price above a certain minimum and a consumer making sure of a bidding war each time she is looking to buy something. Real life transactions have a lot of influence of behavioural factors as well as institutional factors. Some prices are simply a matter of habit, others of arbitrary anchors and so on. On this base case the consumer producer feedback takes place.

In summary, value is not identifiable in an objective sense. Hence a uniform price for a good/service is an arbitrary imposition of simplicity. I think it is not hard to think about constantly varying prices of goods and services across people, place and time. The efficient market enthusiasts will jump at this suggestion and cry ‘arbitrage’. However, insofar as consumer goods are concerned, it is hard to imagine majority of people engaging in an arbitrage about making someone else buy something or hoarding some stuff because it is cheap at that time.

In some cases this already happens – but it is limited to dependence on place. Convenience stores sell things at a major premium to supermarkets. However, time dependence, situational factors and person dependence is almost never factored in. Even more so, the perception effects, network effects and so on are rarely if ever incorporated into pricing. Even the limited space dependence of the type of convenience stores vs supermarkets is a matter of practice – not entirely explained in economic theory.

Brain as a computer

The big question is – is the brain a computer? (not only like a computer)

Turing et al set up the computational theory of computers. Using those ideas, it seems highly unlikely that the brain too is one computer – however complicated. But then there are others like Dennett that argue that the word computer has been used in a narrow context of a top down machine made of cooperative algorithms. In the wider context that allows computational capability embedded in competing modules, the brain, according to this line of thinking, can be safely thought of as a computer.

The other attack on the idea of brain as a computer comes from the ‘thinking’ side. We as human beings think. Computers don’t think, they ‘mechanically’ carry out instructions. Deep down in the software and hardware of the computer, there are only electrons moving about – as per a pre-determined circuitry and well defined rules of logic. This set up, however complex it gets, remains at heart a deterministic machine that at best simulates the idea that it carries out complicated procedures to solve problems – and these problems are actually solved by the ingenious programming designed by humans from outside.

This is partly correct. The computer is ultimately a collection of electrons moving about and the overall impression it gives of immense computing power is simply the effect of the miniaturization of its circuitry and the consequent space efficiency in managing all that in a small box sized CPU of it.

Where this analogy loses track is in forgetting that the brain being ‘more’ than the sum total of its computational/cognitive circuitry is simply a claim, a matter of faith, an unverified hypothesis if that. So while it’s entirely correct that computer is sum total of its billions of logic gates, the brain can also be thought of – in absence of any compelling evidence to the contrary – as sum total of its billions of synapses. There is of course the matter of being ‘inside’ the brain and being able to ‘experience’ this purposeful behavior of human thinking as against the mere dance of electrons through the logic gates. But this thinking commits the usual twin-sins of anthropocentrism and lack of imagination. 

Anthropocentrism in the sense that when seen from inside the brain, of course it is going to seem magical, purposive and more sensible than the computer. This sense, this feeling of purpose and so on is part of its programming. There is nothing magical about it. But then computers don’t think do they? This is where the lack of imagination comes into picture. We are unable to imagine that the much revered thinking of human beings can ultimately be broken down – with a lot of work sure – into smaller computations that individually are simply signals. Being ‘inside’ this, our imagination does not generally extend enough to allow us to see the trees in the woods of our thoughts.

I imagine thoughts to be computations being carried out inside the brain using the signal processing mechanisms built on the infrastructure of neurons and synapses. Clearly the modern computers differ significantly in their architecture and their very organization from the human brain. However, the fact that individual signals are processed in a huge amount to carry out an overall computational or cognitive task is the fundamental common thread between the two.

Another attack is generally from the qualia camp. We ‘perceive’ things – some are red, some are pungent and some are symmetrical. We do not merely compute and measure these things, we actually ‘experience’ or ‘sense’ or ‘perceive’ them. The image of a desk with a phone, remote and small box is ‘real’ in my mind. It might have been arrived using computation by my brain. But the final product is this distinct image that cannot be explained using computational terms. This is the summary of the qualia argument. I do realize there is some unexplained phenomenological account that is needed of this experience. However to me, when seen from an alien’s point of view and from outside, this qualia problem is more curiosity than a fundamental premise of human mind being non-computational. Clearly human mind’s computational architecture is not fully understood by – well human minds! There could be several things that we do not know yet about the details of the perception process that can explain the presence of qualia. It is a sub-problem in my view.

Stating succinctly, my current thoughts on the computational view – the brain is vast collection of neurons and synapses – which act as the logic gates for computations. There are several systems or modules in the brain. Some are nearly autonomous systems (breathing, digestion etc) while others are learned but semi-autonomous – walking, cycling, language etc. Lastly, there are systems that are equipped to handle highly unspecific situations – which are located mostly in the neo-cortex and are most well developed in humans. These systems are an evolution driven feature to survive in the world using one’s wits – i.e. ability to think on the fly using the inputs from the surroundings and computations about a suitable course of action highly customized to that specific instance. This ability to deal with each situation as it turns out differently requires different mode of computation than say the one that deals with digestion or even locomotion.

Connecting my other thoughts about the self written elsewhere, this system also harbours the socially constructed self. The reflector module is inside this system. It is required by this system only – you don’t need reflection to walk or to digest food. The special place of the reflector module inside this system makes human beings believe that they are different from the ‘dumb’ systems of computation carried out in the silicon based computers. When told more about the modules of digestion and locomotion, most humans would grant that these modules are indeed like the silicon based computers. They will most likely still exclude higher thinking (the ‘self’) from this lowly description. Ask an alien though, and it would simply believe that the ‘higher’ thinking is different only in its details from ‘lower’ thinking and the ‘self’ created by the higher thinking is another module inside the brain of the being.

The way mind is computational is very different from the way the brain is computational. For the mind, the logical reasoning and cognitive processing comes at a very later stage of development. The brain has multiple modules – some mechanical (respiration), some purposive (problem solving). The mechanical modules might resemble the silicon based computer in the processing of signals and information. The higher (or those dealing with less deterministic tasks) modules are unlikely to be computational in this way.

For example, if I am solving 2+3 in a silicon based computer, I would use logic gates that help me solve this. That might have a few of those gates at best which will effectively throw the output. If I am solving it in my brain though, the problem is set up in the world of very high level concepts of 2,3 and +. This makes the neuronal support required for it several orders of magnitude larger than that needed for the silicon based computer. This all fine from evolutional point of view because the need to solve 2+3 came up much later for organisms (if at all it can be said to be a need.) The ability to deal with a complex and ever changing environment is their first priority. For that they need the complex modules dealing with ideas. That same module if pressed into the service of solving 2+3 will continue to use its established methods – which from the computing efficiency point of view are highly inefficient.