Showing posts with label near decomposability. Show all posts
Showing posts with label near decomposability. Show all posts

Apr 17, 2012

Modularity, networks and growth of complexity


Herbert Simon and Albert Ando (Simon and Ando 1961) have developed the concept of near decomposability, which is based on the idea that systems of interactions can be separated into groups (modules) according to the strength of interactions. If there are groups of elements among which interactions are much stronger than among other elements, while they show less strong interactions with other groups of interactions, it is assumed that these intergroup interactions can be neglected.

The obvious danger in this assumption is that interactions between groups are neglectable, which may be correct in the short run or under normal conditions but may also be wrong under longer terms and more unusual conditions, which leads under positive feedback to the crossing of thresholds and phase transitions and then may be observed as increased stress, risk and ‘catastrophes’.

While the system can be applied to the analysis of systems it may, under conditions, also be turned around to the explanation of emergence and system change.

Simon-Ando decomposability implies that microstates may be aggregated into (different) macro-states that describe aggregate system behaviour respectively macro-state variables. This is relevant for the analysis of different views, explanations and approaches to the analysis of an issue such as it occurs in science in general and in social sciences in particular.

Decomposability, i.e. separable modularity of a system correlates with flexibility, adaptability and ease of change of a system such as an organization. Thus, decomposability, innovation and the inverse of risk correlate. Decomposability is related inversely with risk as non-decomposable systems are characterized by systemic interlinkages that are more difficult to account for and manage. Near decomposability (Simon ) involves the assumption that interlinkages among a systems (possibly developing) modules can be neglected for analytical and extrapolation purposes. If non-decomposable systems are taken to be decomposable or decomposed according to non-fitting schemes, risk increases relative to a better match between partial model and its extrapolation. It is a viable assumption that interpretation schemes associated with non-fitting problem decompositions based on erroneous models are at the root of individual organizations threatening business failure as well as the economic crisis currently affecting the world economy.

Feb 14, 2012

Complexity, Evolution and Near decomposability

I have been pointing out the relevance of (nearly) decomposable systems, modularity and hierarchy to deal with complex systems before. Some of you had asked for a summary at some point.

I would like to know what you think of the concept and where you deem it useful for our discussions. 

•         It is his story of how variety generation can produce structure (organized) complex results relatively quickly by employing hierarchical relationships in evolution. 
•         It is potentially interesting wrt how costs of interaction between interfaced systems composed of (sub-)systems can be explained and measured
•         It provides a potential basis for the measurement of “complexity” and risk based on the interaction strengths between systems.

Here is a summary of one of the core articles of Simon on the evolution of complex systems based on hierarchy and nearly decomposable system. 
   
It gives a summary of his thinking on this subject inspired by natural systems and the application to social systems. 

His view of the evolution of complex structures is based on the following assumption:

If the existence of a particular complex form increases the probability of the creation of another form just like it, the equilibrium between complexes and components could be greatly altered in favor of the former.
This process (obviously) makes evolution of complex structures from building blocks in hierarchic structures much faster than the independent evolution of complex structures. The result of the process of evolution “employing” such an approach is near-decomposability (again – as it is based on simpler elements that are interacting more strongly internally than externally).

Simon defines near decomposability as follows:

(a) in a nearly decomposable system, the short-run behavior of each of the component subsystems is approximately independent of the short-run behavior of the other components; 
(b) in the long run, the behavior of any one of the components depends in only an aggregate way on the behavior of the other components.

He defines hierarchy as follows:

By a hierarchic system, or hierarchy, I mean a system that is composed of interrelated subsystems, each of the latter being, in turn, hierarchic in structure until we reach some lowest level of elementary subsystem. 

And qualifies the arbitrary notion of the measurement scale of a system wrt measurement / classification of the basic elements (defined as systems) by saying:

In most systems in nature, it is somewhat arbitrary as to where we leave off the partitioning, and what subsystems we take as elementary.

He further qualifies it by comparing to social organizations (which he described before)

Etymologically, the word "hierarchy" has had a narrower meaning than I am giving it here. The term has generally been used to refer to a complex system in which each of the subsystems is subordinated by an authority relation to the system it belongs to. More exactly, in a hierarchic formal organization, each system consists of a "boss" and a set of subordinate subsystems. Each of the subsystems has a "boss" who is the immediate subordinate of the boss of the system. We shall want to consider systems in which the relations among subsystems are more complex than in the formal organizational hierarchy just described. We shall want to include systems in which there is no relation of subordination among subsystems.

which relates to 

NEARLY DECOMPOSABLE SYSTEMS. We can distinguish between the interactions among subsystems, on the one hand, and the interactions within subsystems. ... As a second approximation, we may move to a theory of nearly decomposable systems, in which the interactions among the subsystems are weak, but not negligible.

THE DESCRIPTION OF COMPLEXITY. The fact, then, that many complex systems have a nearly decomposable, hierarchic structure is a major facilitating factor enabling us to understand, to describe, and even to "see" such systems and their parts.

The definition of near decomposability can be mode more formal (where it ordered such that strongly interacting elements are placed near each other):

This article treats of systems that are nearly decomposable--systems with matrices whose elements, except within certain submatrices along the main diagonal, approach zero in the limit. Such a system can be represented as a superposition of (1) a set of independent subsystems ... and (2) an aggregate system having one variable for each subsystem. ...

From the abstract of Simon and Ando's 1961 Aggregation of Variables in Dynamic Systems
which then allows to deal with the analysis and measurement of complex systems:

There is redundancy in complexity which takes a number of forms: Hierarchic systems are usually composed of only a few different kinds of subsystems, in various combinations and arrangements. Hierarchic systems are, as we have seen, often nearly decomposable. 

Hence only aggregative properties of their parts enter into the description of the interactions of those parts. By appropriate "recoding," the redundancy that is present but unobvious in the structure of a complex system can often be made patent. 

This allows us to reduce “the complexity” of the measurement of complex systems by compartmentalizing interactions and effects in areas or “boxes”.

N.B.: If one reads several of Simon's papers on causality and measurement there are a number of Machian perspectives and topics, however Mach's evolutionary / developmental / genetic perspective gets crushed in a systemic classification of the (Simonian) world, where the classification system seems to introduce a static nature. (Please correct me if you think I am wrong!)

I would greatly appreciate your feedback and thank for your attention!

Feb 12, 2012

Is complexity measurement of organizations posible and feasible?


Organizations can be seen as hierarchical systems with business line / unit and departmental ‘modules’ that allow execution of specific functions through specific capabilities concentrated in particular areas. This confers economies through separation of work but also leads to interpretation and filtering problems in non-standard or changing situations - interpretative blindness and inertia are fostered in organizations. Therefore, organizations need to be heterarchical. Particularly in conditions of increased complexity and speed of change we face today. 

Heterarchical structures allow faster and broader interpretation of information, but also demand higher interpretative capabilities by management. Given traditional and often still normal ‘linearly’ organized procedures and structures, these interpretative capabilities determine to a large extent the success of an organization – as Edith Penrose already highlighted.

On the other hand, functional and departmental decomposability, i.e. separable modularity of an organization correlates with flexibility, adaptability and ease of change of an organization.

Does measurement of decomposability (e.g. based on Simon’s near decomposability) allow for a measurement and thus management of organizational complexity? What do you think?