Tag: adoption

Alchian – Uncertainty, Evolution, and Economic Theory

Alchian, A.A. . Uncertainty, Evolution, and Economic Theory . The Journal of Political Economy Vol 58, No 3 pp. 211-221 . The University of Chicago Press . 1950

DPB: firms are exposed to the scrutiny of their environment and they can be adopted by it to a varying extent. The development of the elements in the environment is largely random. Firms are more inert and they develop less. But firms are not passive: they can be adaptive and devise methods to achieve success. One method of adaptation is to imitate organized behavior of a successful firm such that it is hoped that the success comes with the imitated behavior. Another method is to perform trial-and-error at any point in the development. Whether these experiments are successful can only be tried in practical terms. To predict success based on these is impossible because the relations of the firm with the environment are too complex, too variable and too many. This is useful: it corroborates with the idea that the organization of a firm is just-so. The starting point is that chance plays a pivotal role in the development and that personal brilliance has a limited effect for the success of the firm.

Incorporate incomplete information and uncertain foresight as axioms in economic theory. Profit maximization is dispensed with as well as predictable individual behavior. This approach embodies the concept of biological evolution and natural selection. The economic system is seen as an adoptive mechanism that chooses from exploratory actions adaptively, pursuing ‘success’ or ‘profit’. DPB: this sounds like ideas for profit being explored by the economic system through a selection mechanism. The applicability is not only to the standard situation but also to what is considered aberrations by the existing theory. The postulates of accurate anticipation and fixed states of knowledge are removed. Structure of the article: 1) where foresight is uncertain, the principle of ‘profit maximization’ is meaningless as a guide to action. 2) Construction begins with the introduction of the concept of environmental adoption: a posteriori most appropriate action based on the criterion of ‘realized positive profits’. The concept of environmental adoption is then fused with individually motivated behavior based on uncertainty and incomplete information: ‘Adaptive, imitative, and trial-and-error behavior in the pursuit of ‘positive profits’ is utilized rather than its sharp contrast, the pursuit of ‘maximized profits’ [Alchian 1950 p 211]. DPB: this is very interesting, because it allows for just-so elements, namely trial and error of economic practice. 3) Conclusions and conjectures.

1 Profit maximization not a guide to action

Economic agents are assumed to use demand and supply curves, but their position and slopes are uncertain. Under uncertainty one action can have various results, according to a distribution. After the action, the result will come to the fore. But the (distributions of the) results of different actions will overlap. To maximize a distribution does not exist. To select an action that generates maximum profit is only possible if there is no overlapping distribution. If they do overlap then the result does not point to one action. The task is therefore not to maximize profit, but to choose an action that leads to an optimum distribution, leading to a positive profit goal definition.

2 Success is based on results not motivation

‘Realized positive profits, not maximum profits, are the mark of success and viability. It does not matter through what process of reasoning or motivation such success was achieved. The fact of its accomplishment is sufficient. This is the criterion by which the economic system selects survivors: those who realize positive profits are the survivor; those who suffer losses disappear’ [Alchian 1950 p 213 (emphases by the author)]. Positive profits accrue to those who are better relative to their actual competitors (not a hypothetical ideal one). When uncertainty is larger the profit is more likely to go to the more venturous and lucky and less to the logical and well-informed. Concluding: a) success points at relative superiority and b) not motivation but circumstance may lead to the positive profit. Competitors with the most appropriate conditions will be selected by the environment for testing and adoption.

3) Chance or luck is one method of achieving success

Determination of the situation and the appropriateness depends on chance. Ability to adapt oneself to the situation is another element. The survivors may appear to have adapted to the environment, or the environment has adopted the survivors (emphasis DPB). A useful example is presented: travelers have to choose a path from one city to another. Petrol isn’t available on all of the paths. The travelers don’t know on which path petrol is available and on which there isn’t. Only the travelers on the path with petrol can travel, the others are not. They are considered smart, the others are not. When the petrol supply is changed to another path, then the latter travelers move and the others have to stop. Now these ones are considered the smart travelers and the others are foolish. The environment, namely the path infrastructure and the petrol supply, adopts the travelers. Their traveling skills can only be applied when the environment enables them to, they are ‘adoptable’. They travel when the environment ‘adopts’ them, and in that case they can show ‘their best traveling’, but whether they do get the opportunity is decided by chance. DPB: can this be translated into a situation of attraction and repulsion? A path with petrol attracts travelers: they are given the chance to travel, and in a particular direction. They are restricted by the availability of petrol: purposeful action is attracted to it, lack of it is repelled from it. The ‘correct’ direction of travel can be established if the availability of petrol on particular paths is certain. By determining the environment, the success of the travelers can be determined as well as the conditions conducive to it.

4 Chance does not imply non-directed, random allocation of resources

It might seem that the facts of life deny chance to be the deciding factor for the adoption principle in the economic system. Size of firms and heritage seem to indicate wisdom and foresight. Mathematician Borél has shown that these examples do not provide evidence against luck. If a million pairs play toss for 8 hours a day and one toss takes 1 second and the play stops if the winner of the first toss is equaled, then 100 pairs are still in play after 10 years. And, if the game is inherited, statistically 12 pairs play after a thousand years. So chance is likely to play a part in the survival of a 100 years old firm. There are not too many but too few firms to corroborate this analysis. Note that a) if all economic actions were random, the variety would be large and therefore the probability is large that the path of perfect foresight will turn out to be one of the survivors without him ever having had the intention. b) if some or even all of the participating firms behave non-random then the set of their behaviors is indistinguishable from a random set in terms of variety. c) A chance dominated model does not mean that the behavior cannot be predicted or explained. ‘It is sufficient if all firms are slightly different so that in the new environmental situation those who have their fixed internal conditions closer to the new, but unknown, optimum position now have a greater probability of survival and growth’ [Alchian 1950 p 216]. DPB: this matches very well the logic of process metaphysics. Where there are differences there is a chance that something will change. In that case an attractor can emerge from the changing environment to which the kind of firms, because of its ’internal conditions’, and knowingly or not, is attracted or repelled. This occurs because there are repeated trials and because there are more firms with a similar characteristic that have an elevated chance or landing in that basin of attraction and on that attractor. d) Not the characteristics of the firms change, but the characteristics of the set of firms that survives the new environmental circumstance. e) Individual motivations are sufficient but not a necessary condition. Instead what is required necessarily is the set of requirements of the economic circumstance.

5 Individual adapting via imitation and trial and error

Purposive motivation and foresight are added to the extreme model of adoption (and not to merge it with perfect foresight &c. and profit maximization). It is assumed here that the objective is the sufficient condition of realized positive profit. That is the condition for survival (not profit maximization). The fulfillment of the pursuit of profit is rewarded with survival. Only perfect knowledge of past results and awareness of the present do not guarantee perfect foresight: chance is a determining factor. As a consequence modes of conscious adaptive behavior replace this knowledge: a) common elements of behavior associated with the successes of successful enterprises are imitated. This is motivated by the absence of clear-cut criteria, a very large number of them, their variability, lack of room for trial and error, &c. Also imitation relieves one of the need to really innovate and be responsible for the outcome if it fails. ‘Unfortunately, failure or success often reflects the willingness to depart from rules, when conditions have changed; what counts, then, is not only imitative behavior but the willingness to abandon it at the ‘right’ time and circumstances. Those who are different and successful ‘become’ innovators, while those who fail ‘become’ reckless violators of tried-and-true rules’ [Alchian 1950 p 218]. DPB: behavior associated with success is replicated: perceive success and behavior, define which behavioral elements determine success and how, define the rules for own behavior, mimick them as long as required. b) trial and error is a second type of adaptive behavior. Trial, and with ensuing success continuation of, and with a lack of success a change of action. But firstly trial must be recognizable as success or not (local optimum). Secondly there can be no intermediate descent or the approach will be abandoned. Both conditions are not likely in the case of economic life. A changing environment prevents one to compare some course of action to the predefined conception of success. These elements frustrate a trial and error process, because that is a survival and death situation, not a personal optimization approach. ‘Success is discovered by the economic system through a blanketing shotgun process, not by the individual through a converging search’ [Alchian 1950 p 219]. DPB: just-so, nomad/monad, individuation. Variation is achieved because imitations are imperfect. ‘All the preceding arguments leave the individual economic participant with imitative, venturesome, innovative, trial-and-error adaptive behavior. Most conventional economic tools and concepts are still useful, although in a vastly different framework – one which is closely akin to the theory of biological evolution. The economic counterparts of genetic heredity, mutations, and natural selection are imitation, innovation, and positive profits’ [p 220].

6 Conclusions and summary

First some behavior (organization) must be submitted to the economic system (mutation) and then tried for its viability (natural selection). These appear to be interrelated: if the probability for viability is higher then the probability for action being taken is higher also, but that is not necessarily so, because there is no for ‘inner directed urge towards perfection’. What counts is not the plans for perfect action but trial of promising action, because from there success is selected. That proven success there can lead to ensuing action. The economist can know effects of changes in the environment on the economic participant, even if he doesn’t know how the participant takes his decisions, by inferring the requirements of the environment. In other words: which organization is adopted by the conditions of that environment.

PS: exaptation (the original term pre-adaptation was replaced because it seemed to suppose intentionality) is the assigning a new function to an existing trait. For instance the feathers of a bird initially served a purpose for insulation and only later supported flight.

Simon – The Architecture of Complexity

Simon, H.A. . The Architecture of Complexity . Proceedings of the American Philosophical Society, Vol 106, No 6, pp. 467 – 482 . 1962

Development of a general systems theory to find out which abstracting properties from all of them can apply to all kinds of systems. Do diverse systems have anything non-trivial in common? This is addressed by ideas under the umbrella of cybernetics (if not a theory than at least an interesting point of view). The goal is to cast some light on the ways complexity exhibits itself wherever it is found in nature. The rough description of a complex system used here is: a system made up of many parts which interact in a non-simple way. In such systems the whole may be more than the parts in a pragmatic sense: ‘In the face of complexity, an in-principle reductionist may at the same time be a pragmatic holist’ [Simon 1962 p 468]. How complexity frequently takes the form of hierarchy is discussed in four sections: 1) frequency of the occurrence of hierarchy in complex systems 2) hierarchic systems evolve more quickly than non-hierachic systems 3) dynamic properties of complex systems and they can be decomposed into subsystems 4) relation between complex systems and their descriptions.

>HIERARCHIC SYSTEMS

A hierarchic system is a system that is composed of other interrelated hierarchic systems. DPB: a hierarchic system integrates other hierarchic systems until some lower, elementary level of subsystems is arrived at. What that level is, is somewhat arbitrary and how it can be done is a subject of this article. Hierarchy is often referred to the structure where systems are subordinated by a relation of authority to the system they belong to. This means the existence of a boss and subordinate subsystems. Each system has a boss who is subordinated to the boss of the system. This is a formal approach to hierarchy. ‘I shall use hierarchy in the broader sense introduced in the previous paragraphs, to refer to all complex systems analyzable into successive sets of subsystems, and speak of ‘formal hierarchy’ when I want to refer to the more specialized concept’ [Simon 1962 p 468].

>>Social Systems

One kind of hierarchy in social sciences is the formal organization of businesses &c. Another is families, tribes, clans, &c.

> >Biological and physical systems

Cell-up Cell>tissue>organ>system. Cell-down: Cell>nucleus>mitochondria>membrane>microsomes.

Elementary particles, Planetary systems

A gas is seen as a random distribution of complex systems, namely particles.

Hierarchy refers to a system with a moderate number of subsystems with their subsystems (a diamond is a flat hierarchy with many subsystems, and atypical). The number of subsystems subordinated to the system is the span of that system. If the span of a (sub) system is wide it is flat at that location. A diamond has a wide span / is flat at the crystal level, but not at the molecular level. Biological and physical systems differ from social systems in that the first are described in spatial terms and the second by defining who interacts with whom. This can be reconciled by defining hierarchy by intensity of interactions.

>>Symbolic systems: Books>Chapters>Paragraphs>Alinea>Words>Letters, &c.

>THE EVOLUTION OF COMPLEX SYSTEMS

Watch maker 1: One system. When assemblage is interrupted the entire watch falls apart. Watch maker 2: Subsystems of 10 subsystems each. When assemblage is interrupted the subsystem at hand falls apart. This one is more likely to survive.

>>Biological evolution

‘The time required for the evolution of a complex form from simple elements depends critically on the numbers and distribution of potential intermediate stable forms’ [Simon 1962 p 471]. Comments: a) no teleology is suggested and the structure can come from random processes. When complex forms once existent become stable they give direction. But this is survival of the fittest, namely survival of the stable b) not all large systems appear hierarchical c) the evolution of complex systems from simple elements implies nothing concerning the change of entropy: free energy can be taken up or generated by the evolutionary process

>>Problem solving as natural selection

‘Problem solving requires selective trial-and-error. .. In problem solving, a partial result that represents recognizable progress toward the goal plays the role of a stable sub-assembly’ [Simon 1962 p 472]. Human problem solving involves only trial-and-error and selectivity. The selectivity derives from heuristics to suggest which paths to try first.

>> The sources of selectivity

‘When we examine the sources from which the problem-solving system, or the evolving system, as the case may be, derives its selectivity, we discover that selectivity can always be equated with some kind of feedback of information from the environment’ [Simon 1962 p 473]. DPB: the approach to modeling evolution is the same as that to modeling problem solving. There are two paths of selection in problem solving: a) various paths are tried out, the results are noted and this information is used for further search and b) using previous experience: doing the same paths that lead to an earlier solution. In this way trial-and-error is reduced or eliminated. The closest analogue of this in organic evolution is reproduction.

>>On empires and empire building

When an empire breaks up, it doesn’t tend to fall apart into its smallest elements but into the next scale of subsystems.

>>Conclusion: the evolutionary explanation of hierarchy

Systems will evolve from stable intermediate forms faster than from basic elements to form hierarchies, the subsystems based on the intermediate forms. Hierarchies have the time to evolve.

>NEARLY DECOMPOSABLE SYSTEMS

A distinction can be made between the interactions within subsystems and between them. Their intensity and their frequency is different to orders of magnitude. Employees within the formal organization of a department have more and more intensive contacts than employees of different departments. The decomposable case can be used as a limit over a wide range. In the nearly decomposable case the interactions between the subsystems are weak but not negligible. From the latter case these can be proposed: a) the short run behavior of the subsystem is independent of that of the other subsystems and b) in the long run the behavior of a subsystem depends on the behavior of the others in the aggregate. This is illustrated with an insulated house within which there are somewhat insulated rooms within which there are hardly insulated cubicles. A change of the temperature in the rooms, induces a rapid change of the temperature between the cubicles, but a slow change of temperature between the rooms. If a complex system can be described with a nearly decomposable matrix then the system has the properties a) and b) above.

>>Near decomposability of social systems

Most of the communication channels in formal organizations are between employees and a very limited number of other employees. The departmental boundaries are assumed to assume the same role as the walls in the thermal insulation example.

>>Physico-Chemical examples

‘The theory of the thermodynamics of irreversible processes, for example, requires the assumption of macroscopic disequilibrium and microscopic equilibrium, exactly the situation described in our heat-exchange example’ [p 476]. DPB: how does this work?

>>Some observations on hierarchic span

Suppose that the elements of a system have properties for stronger bonds and for weaker bonds and that the stronger bonds exhaust through the bonding. Subsystems form through the strong bonds until they are exhausted. Then the subsystems will be linked by the weaker second-order bonds into larger systems. In social systems the number of interactions is limited by the serial character of human communication (one at the time) and limitation on the time consumption involved in a role and hence of the number of roles one can handle (one can have a group of friends consisting of a dozen but not hundreds).

>>Summary: Hierarchies tend to be near-decomposable.

>THE DESCRIPTION OF COMPLEXITY

People draw complex objects in a hierarchical way. The information about the object is arranged hierarchically in memory, like a topical outline. DPB: re active association. When information is presented in this way the relations between a subpart and another subpart can be presented and between subsubparts within each. Information about reations between subsubparts of different subparts is lost.

>>Near decomposability and comprehensibility

By representing parts hierarchically little information is lost (re b, aggregate effect above). Many complex systems have a near-decomposable, hierarchical structure. That enables us to see them. If complex systems exist that are not so structured then they are unobserved and not understood. ‘I shall not try to settle which is chicken and which is egg: whether we are able to understand the world because it is hierarchic, or whether it appears hierarchic because those aspects which are not elude our understanding and observation’ [Simon p 478]. DPB: the processes that brought forth our powers of perception and the processes in nature are fundamentally the same.

>>Simple descriptions of complex systems

There is no conservation law that prescribes that the description of a complex system should be as complex as the system itself. Example of how such a system can be described economically, or, in other words, how it can be reduced. This is only possible if there are redundancies in the system. If it is completely unredundant then the system is its own simplest (most economical) description and it cannot be reduced. DPB: this notion of reduction is exactly the opposite of the notion used by Ashby. He uses reduction to indicate the opposite of organization. That which is not organized can be reduced (away) until organization remains. This is the mathematical definition of reduction. Here it is the opposite: whatever is redundant leaves room, or in other words can be reduced to, a rule. Three forms of redundancy are: a) hierarchic system is often assembled from a few kinds of different subsystems in various arrangements. DPB: this is a form of repetition of the components used. b) ‘Hierarchic systems are often nearly decomposable. Hence only aggregative properties of their parts enter into the description of the interaction of those parts. Not the lower level properties of the composing elements of the parts play a part in the interactions of the components at the higher levels. A generalization of the notion of near composability might be called the ‘empty world hypothesis’: most things are only weakly connected with most other things’ [Simon 478]. DPB: This means that some properties of the subcomponents of a complex hierarchical system, which are themselves built of subcomponents, enable interaction with other subcomponents, and form the complex hierarchical system that they are a subcomponent of. But those enabling properties of the subcomponents are properties of, or based on properties of the subcomponents of the subcomponents that form the complex hierarchical system. ‘The children are not allowed to participate in the discussion between the family elders’. Given that emptiness can be described by the absence of a description it can be described economically. DPB: how is this a form of repetition? The aggregative properties of the subcomponents of the system repeat, and they are based on repeating or comparable properties of the sub-sbcomponents. c) Redundancy can originate in a constant relation between the state of a system and a later state of it. DPB: this is a form of repetition of the behavior of the system. It can be a literal repetition or the lingering of a system like a kind of an after image of the previous state. In any case the current state can be compared with the previous one and with the next state also. Cognition is the application of its powers to compare, identification of redundancy, so as to perform a (re)cognition of recurrence of coherence, namely pattern. On a devised continuum of cognition ‘a suspicion’ is on the one extreme, where a pattern merely reminds of something such that it cannot be predicted for what it ‘is’ or whether it will occur with any certainty. ‘Knowing’ is at the other extreme, where the pattern is known and its occurrence can be predicted with a high level of certainty.

>>State description and process description

State -: a circle is an object of which all points are equidistant from one point. Process -: hold one arm of the compass in place, rotate the other arm until is is back at the initial point. ‘These two modes of experience are the warp and weft of our experience. .. The former characterize the world as sensed; they provide the criteria for identifying objects, often by modeling the objects themselves. The latter characterize the world as acted upon; they provide the means for producing or generating objects having the desired characteristics. The distinction between the world as sensed and the world as acted upon defines the basic condition for the survival of adaptive organisms. The organism must develop correlations between goals in the sensed world and actions in the world of process. When they are made conscious and verbalized, these correlations correspond to what we usually call means-end analysis. Given a desired state of affairs and an existing state of affairs, the task of an adaptive organism is to find the difference between these two states, and then to find the correlating process that will erase the difference. Thus, problem solving requires continual translation between the state and process descriptions of the same complex reality’ [Simon 1962 p 479]. DPB: this is my equalizing of differences. It refers to adaptive organisms that is autopoietic systems. The translation between state and the process are then the same as the recurring consequence of structure and operations: the description of what it is and the description of what it does, &c. Refer to this in the main theory. ‘We pose a problem by giving the state description of the solution. The task is to discover a sequence of processes that will produce the goal state from an initial state. Translation from the process description to the state description enables us to recognize when we have succeeded’ [Simon 1962 p 479]. DPB: can this be coupled to challenge propagation?

>> Ontogeny recapitulates phylogeny

If genetic material is seen as a program, it: a) is self-reproducing, b) developed by Darwinian evolution. A human develops gills and then use them for other purposes. Instruct a 20th century workman to build a car by what he knows: start with a cart, remove the singletree then build a motor onto it, then a transmission. &c. DPB: this does not necessarily apply to the memetic instructionset of a firm. Or is it, sometimes routines are in place that stem from previous versions of work instructions that are no longer in place: ‘The generalization that in evolving systems whose descriptions are stored in a process language, we might expect ontogeny partially to recapitulate phylogeny has applications outside the realm of biology. It can be applied as readily, for example, to the transmission of knowledge in the educational process. In most subjects, particularly in the rapidly advancing sciences, the progress from elementary to advanced courses is to a considerable extent a progress through the conceptual history of the science itself. Fortunately, the recapitulation is seldom literal – any more than it is in the biological case. .. But curriculum revisions that rid us of the accumulations of the past are infrequent and painful’ [Simon 1962 p 481]. DPB: this is an important thought concerning the execution, namely the enactment of memes ad how the are restricted by the actual state of affairs, when the firm is operational.