MANAGING MESS AND INFLUENCE DIAGRAMS

Whenever and wherever we have a system we have a potential mess problem. Systems are designed taking each single participant or entity into context, finding the relationships with other entities, finding the optimum path etc. This is a rather constricted way of designing due to which it’s difficult to foresee a mess coming up. When the entire system is integrated and put to uses then we realize what mess problems crop up.

Again for solving these mess problems people break up the system into parts and try to solve the problem. This is a faulty approach for solving mess-related issues because:

v Unless we see the system and the mess in it’s entirety we run the risk of not solving all the problems fully.

v Further systems are dynamic so unless a robust solution is found out we can have mess develop after further use of the system.

Examples of messy networks include traffic networks, internet and LAN networks, switching controls etc.

Below is a diagrammatic representation of a mess in a typical computing network

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(Source: www.acm.pku.edu.cn)

Messes are complex and dynamic systems and each mess shows these characteristics:

1- Merge Point: Point where 1 or more entities merge.

2- Burst Point: Point of emergence of 1 or more entities.

3- Loops: Loops are inter-connected entities affecting each other. Loops are again of 2 types: Positive loops are those where actions in one entity cause an action in a similar direction in another entity. Negative loops are those whose actions cause a change in an opposite direction.

A system drawn as above with loops and points is called an influence diagram (ID). A typical ID looks as below.

An ID contains arrows and nodes which are of different shapes and convey different meanings.

(Source: www.lumina.com)

The 1^st step of avoiding a mess is to draw an Influence Diagram for the system. Then with the help of reference projections we can show what the future of that system will look like. What this implies is that the system is projected into the future and the mess revealed. This method is based on two faulty assumptions as follows:

a- System will not change its current configuration.

b- System will change only as expected.

This will help in knowing where we are going to in the future and hence we can avoid any messy situations. Further this method opens our eyes towards the formation of mess which would have gone un-noticed had we not been able to see the future condition of the system. So knowing the future will help in avoiding the mess.

Finally to conclude mess management requires lot of creativity and planning. All methods to manage a mess ultimately depend on the planner’s ability to perceive and foresee the possible messy situations and act accordingly.

Reference:

Ackoff, R.L.(1986). Management in Small Doses, Wiley, NY.

Network Mess. http://www.acm.pku.edu.cn

Influence Diagrams. http://www.lumina.com

(The work above is of my independent doing and all sources have been duly acknowledged.)

LIVING SYSTEMS MODEL(LSM)

( This assignment is the outcome of my independent work. All sources have been duly acknowledged.)

An organization is a complex system. To understand the dynamics of a complex system and enable the growth of an organization, scientific help in the form of models are required by people who have the authority to make decisions and pave the path for the future. During the course we studied about lots of models like the Interpretive Structural Model (ISM), SNAC (Stakeholders, Needs, Alterables, and Constraints) etc. The idea of a model is to leverage the scientific analytical tools with practical real-life dynamic problems to solve strategic organizational issues. Ultimate goal is that the model should help in decision making for the senior managerial level people.

Living Systems Model (LSM), developed by James Miller in 1978 is a cross-discipline model which studies the structure, functions, behavior of living beings and extends the same to an organization’s complexities. Both living beings and organizations are complex, dynamic, possess several inter-dependent entities and are ever-changing. Due to these similarities various scientists and researchers have attempted to map the dynamics of a living being to an organization. The advantage with living beings is that one can easily observe them, notice the changes happening and see the causal relationships shaping their systems all in a perceivable finite time-frame. Organizations are abstract entities and hence studying them is tougher.

Living Systems Model (LSM) is based on weak signals research (WSR) and uses the concept of information as similar to the concept of message in WSR. LSM has important implications in understanding the various nuances of an organization which will help in effective development of strategy.

Uses of LSM have been found in hospitals (Merker & Lusher, 1987), several public schools in a community (Banathy & Mills, 1985), a public transportation system (Bryant & Merker, 1987) etc.

The core idea of WSR is that
• Signals become information or messages only when they reach the intended receiver.
• Many signals come into an organization but only a few are able to be decoded and related to the organizational issues.
• Even if a weak signal reaches a person its rare that the person is the intended receiver.

Weak Signals have two perspectives:

• Claude Shannon’s view as a measure of uncertainty or surprise
• Norbert Wiener's view as a measure of the degree of order (or complexity) in the system

There are some terminology used in LSM which are also referred to as subsystems and without going deeper into each I will just try to understand each of these subsystems.
Input Transducer (IT) – Subsystem which brings information into the system.
Internal Transducer (NT) – Subsystem which receives information about other subsystems.
Channel and Net (CH) – Subsystem which transmits information in the system
Decoder (DC) – Subsystem which changes the information received into “private” pieces of information for usage by different types of receivers.
Associator (AS) – Subsystem which associates the information
Memory (ME) – Subsystem which stores the information
Decider (DE) – Receives information and gives output for functioning of the system.
Encoder (EC) – Receives the information from decoder and encodes it
Output Transducer (OT) – Subsystem which transmits information out of the system.

Each of these sub-systems has an important function to play in a living being to develop effectively. Similar is the case for an organization. If development is required then these subsystems must be present and doing their job. Without going into the details of how each subsystem works I will focus on a few subsystems and their implications on an organizations development of strategy.

Associator: Main function is to group messages into particular “ecosystems”. Then it compares these messages with those already present. In this it looks out for similarities, differences, discrepancies, logic of messages etc. In the process it identifies threats and opportunities. It’s easy to understand its usability in an organization. Associator will help in effective finding of opportunities for the organization. Since there is a dedicated system for this purpose in an organization this will continuously help in review of the past and present data and pave the future path.

Decider: Organizations and living beings do not always have a clear decision to implement or path to follow. There is always lack some clarity of vision surrounding the outcome. In the terminology of LSM model there is an element of surprise and one doesn’t know of all options available. Deciders help in identifying what is called as a “fitness landscape” which comprises of all states that are possible. “Peaks” on a fitness landscape represent those states that are more fit or bound to be more successful than others. But the fitness landscape keeps on changing for an organization due to macro and micro events concerning the business of the organization. In order that organizations keep moving on the right track the decider system has to continuously improve the fitness landscape and find more peaks.

Now it must be getting apparent how each subsystem in LSM has a role to play in strategic decision-making for an organization. An important use of LSM is in decoding conflicting signals. Conflicting signals slow down the process of decision making and also could lead to erroneous decisions hence its imminent that signals be correctly understood.

Finally to conclude I would say that LSM is in itself a huge area of research and my understanding has been limited to grasping the concepts of it. To fully understand LSM one needs to look at the organizations where LSM has been used for decision-making and management.

Reference:

Coffman, Bryan S.(1997). James Miller's Living Systems Model, An Interpretation and Application of the model to weak signal research by Colloborative Design and Group Genius Processes.

Miller, James Grier & Miller, Jessie L.(1997). Applications of Living Systems Theory.