Simulation and Modelling in a Knowledge Context

	Simulation and Modelling Limitations of Models Mathematical Models Describing Concepts Situation Calculus Types of Reasoning Reasoning in Expert Systems Expert System Architectures The Knowledge-base If-then rule based systems Uncertainty in Rule-based systems The Inference Engine Search strategies Selecting backward or forward chaining Applications of Knowledge-based systems Dynamic Programming Multi-agent Models and Simulation

Simulation and Modelling

Simulation and modelling is increasingly being used by both private and public organisations to help make decisions and formulate strategies. The popularity of the simulation approach is perhaps due to its ability to allow the modelling of different scenarios which may feed back into decision makers' understandings of the problem. In this sense, these tools may be used in multiple phases of classical decision making. However, there are dangers inherent in using models and simulations as a basis for decision making. In some cases such models are perhaps useful in gaining an understanding of the general properties of some types of systems, but as mentioned in earlier lessons, specific situations can make a nonsense of general rules. Therefore one must be aware of the limitations of models in that not all the complexities of reality can be included. How significant the effects are of leaving information and processes out of the models will depend on both the assumptions of the model and the level of understanding of the system as well as the complexity of the system being modelled. In this topic we will look at simple modelling and simulation tools that can be, and are, used in small to large businesses everyday, through to larger more sophisticated tools that are used by social and business planners.

Limitations of Models

Again it needs to be emphasised that simulations are typically nothing more than a model of the system. Pidd (2009) defines a model as a:

representation of reality intended for some definite purpose.

He includes the phrase "definite purpose" because inevitably some aspects of reality are left out, and those aspects may make the model entirely unsuitable for some purposes. The model most likely leaves out even some aspects relevant to the intended definite purpose. This raises two other issues. Turban et al. (2007) points out that simulation models can be expensive to construct, may require specialised expertise (that may be difficult to obtain) and in the end, may not provide an optimal solution.

Mathematical Models

While physical models are possible most modelling today is conducted using computers. These models are typically based on mathematical principles. Here we will briefly examine those principles along with common modelling techniques to gain an understanding of the underlying problems addressed by some mathematical methods and the difficulties in addressing them. To begin with any model of reality must represent some of concepts that exist in reality. In the next section we look at some ways how concepts can be represented abstractly. Note, this is not a definitive review. Some methods such as linear equations and genetic algorithms are not discussed here.

Describing Concepts

This material is based on Hurley (1991).
Definitions can be extensional (logical denotation) or intensional (logical connotation).
When using extension, we describe (define) a concept based on the members of the class. When using intension, we describe a concept using properties or qualities of the class.
Types of extensional definition are:

• Demonstrative (ostensive) - pointing to members of the class (limitation: examples must be present, may lead to confusion due to common properties of available examples. eg: the indicated chairs may all be wood, may lead to misunderstanding concept chair to mean "made of wood")
• Enumerative - listing or naming members of the class (may be a partial or complete list). Limitiation: cannot list all sets (eg. set of all stars), some members may not have names (eg. insects).
• Subclass - name subclasses of the class (may be a partial or complete naming).

Because of the first limitation, extensions can only ever suggest meanings, but cannot determine them. Therefore, intensional definitions (connotations) are used.
Types of intentional definition are:

• Synonymous - this can be used if there is exact equivalent (eg. "Intentional" means "willful"). Limitation: there is not always such a synonym.
• Operational - specifies operational procedures which can be conducted to determine if something meets the concept. (eg. something is harder than something else if the first can scratch the other when rubbed together). Intended to tie down abstract concepts in reality. Limitations: not always possible, define an operation to demonstrate the concept of "love". Also, captures only part of the meaning of the concept.
• Genus and difference - in logic "genus" means "class" and "species" means "subclass". The difference (or specific difference) distinguishes members of the subclass from the superclass. (eg. Species: Husband, Genus: Man, difference: married).

There are a number of different kinds of definition, all of which can be achieved using intentional means:

• Stipulative - assigns a meaning to a word or symbol. Eg: 10^3 means 10 x 10 x 10. This is an arbitrary assignment and as such, others may use the words in other ways (for example, times (x) has a different meaning when used for cartesian products than it does for common multiplication).
• Lexical - reports a meaning a word already has in language. Such meanings may be vague or ambiguous (what is the difference?).
• Precising definition - reduces vagueness of a word or concept - egs. poor, "moment of death".
• Theoretical definition - provides a way of viewing or characterising the entity or entities involved. eg. "heat means the energy associated with the random motion of the molecules of a substance". These often entail deductive consequences and possibly sets of experiments. Other terms which have been given different theoretical definitions reflecting an individual's view are: "God", "mind", "cause" and "intelligence".
• Persuasive - assigns an emotive description to a word indicating it has, or should have, that meaning every day use. eg: "Abortion means the ruthless murdering of innocent human beings".

Importantly, of the different kinds, stipulative and theoretical definitions are neither true nor false.

Situation Calculus

The following sections are based primarily on Chapter 5 of Klein and Methlie (1995) other sources are linked as necessary.

Types of reasoning:

There are different types of reasoning:

• Deductive: if A is true then B is true. This is modus ponens: A is a sufficient, but not necessary condition for B, and B is a necessary but not sufficient condition for A. In English: if we accept the premises are true then we accept that the conclusion is true. The converse of this is modus tollens : if we accept that B is false, then we must accept that A is false. eg: at 11.00 am it is daytime. In this case, if it is not daytime, then it cannot be 11.00 am. The statement could be naively interpreted as Premise: it is daytime, Conclusion: it is 11.00 am. In which case it is clearly incorrect since it implies that if it is not 11.00 am then it is not daytime.
• Abductive: A process similar to modus tollens but which allows illegal (i.e incorrect) inferences. In abduction, B is not a necessary condition for A. Abduction allows multiple possible explanations for something. In abduction we may entertain incorrect inferences such as: Fact (conclusion): it is daytime, inference: it is 11.00 am.
  Of course it could be any other time of day also, but it is plausible (at least) that it is 11.00. In abduction, we take as a conclusion something which allows many possible premises (some of which are incorrect):
  Premise * ------- 1 conclusion
  Another example: all men are mortal and Socrates is mortal. The conclusion here is that Socrates is mortal, from which we incorrectly infer the premise that: Socrates is a man (Socrates is in fact a horse, although he could be any other mortal creature, therefore the multiple possible answers, in this case, premises). Abductive reasoning is commonly used by people to generate plausible hypotheses for things. Eg: Fact (conclusion): Holden's sales are bad. Invented premise: they must make poor cars.
• Inductive: A process similar to modus ponens but which allows invalid premises. In induction A may, or may not be, a sufficient condition for B. eg: Premise: a vehicle has four wheels. Conclusion: it is a car. The vehicle may also be a truck. As with abduction, induction also allows multiple possible answers, this time we accept as a premise something which is not sufficient for its conclusion so we allow multiple possible conclusions (some of which are incorrect):
  Premise 1 ------- * conclusion
  This is the Sherlock Holmes approach (he does induction, not deduction as he claims): "Ah, the man was wearing glasses, it must have been Mr Smith!". Inductive arguments involve probabilistic reasoning (Hurley 1991).

For a discussion of necessary and sufficient conditions see: here
For general logic see: here

Reasoning in Expert Systems

Expert systems take in data from the user using a series of questions to which the user provides answers (the system controls this process) The system attempts to provide expert knowledge about that data and to explain its reasoning. The typical example is of medical diagnosis where the system takes in symptom information and provides a diagnosis and an explanation of that diagnosis.
Expert systems are different from classical decision support systems (DSSs), in which the user is in control and poses questions to the system (like our spreadsheeting exercises in the previous lesson). DSSs can therefore be less structured. In DSS's the user is the expert so they provide their knowledge, in Expert systems the system is the expert so the user is more passive.
Expert systems are built using modus ponens (but typically cannot reach conclusions using modus tollens) and using this rule chain together rules using the conclusions of one rule as the premises in another. This forms a line of reasoning from the premises to the conclusion of the system. This is a largely mechanical process as we shall see when we return to expert systems in a later section. This process is refered to as drawing inferences and is typically conducted by an inference engine.
However, as we have mentioned, if our rules are not quite correct (or too simple) we may have multiple possible answers. In these cases it is necessary to somehow pick one answer over the others. This is called conflict resolution. Solving conflicts is one requirement of an expert system, the rest are covered in the next section.

Expert System Architectures

Expert systems consist of:

• a knowledge base
• an inference engine
• a user interface or shell (for both system developer and end-user)
• a user.

The Knowledge-base

One of the hardest problems in constructing expert systems is extracting the knowledge from the experts, often they are unaware of their own reasoning processes which occur sub-cognitively. However, given that a good knowledge engineer can extract the expert's knowledge there is the problem of how to represent it.
Three possible knowledge representations are:

• If-then rule based systems; and
• Propositional systems (semantic nets);
• Frame-based (object-oriented) systems.

If-then rule based systems

One of the most common types of expert system representations are if-then rules. These are quite simple, and can be used for a variety of purposes:

• Inferential knowledge: if premises, then conclusion;
• Procedural knowledge: if situation then action; and
• Declarative knowledge: if antecedent then consequent.

Rules typically contain a set of conditions joined using logical connectives such as AND, OR and NOT.
Long term memory in such a system can be organised as lists of attributes and values. To represent a particular piece of data you specify the object-id, the attribute and value of the attribute. This is similar to object-based programming languages (without inheritance).
Rules can therefore be applied to sets of objects (classes of objects) as follows:

IF (object-id, loan-size < MIN_LOAN)

THEN (object-id, loan-approved = false).

Rules allow inference from a set of premises. Given some data rules can be matched, which leads to conclusions being drawn, the current facts and conclusions are stored in short-term memory (the object attribute values, called a work-space or blackboard) where they can be used to match the conditions of other rules.
This type of process where an initial set of facts is used to generate further facts through the application of rules is called a major control cycle .
The major-cycle involves using the available data to match the conditions of rules, of all these rules some may need to be selected to execute (have their facts in their conclusions added to the work-space).
Rule based systems (in some cases called production systems ) therefore consist of the following three steps being repeated in each major-cycle:

• match - find which rules conditions are matched by data in the work-space
• select - of all the matched rules select those which appear most useful (this may involve some conflict resolution, since rules may contradict each other)
• execute - update the work-space with the conclusions of executed rules being added as new facts in the work-space.

This continues until a suitable solution is reached, or the system concludes it cannot find a solution. The major cycle chains together the rules of inference. We talk more about what happens in this cycle in a later section on "The Inference Engine".
Note, that there may be facts stored in the system, and that new facts can be generated. These new facts form the short-term memory of the system and comprise knowledge that has been infered by applying rules to stored facts. In some systems this infered knowledge can be added to the long term memory of the rule base to save having to repeat those inferences for later queries. This idea can be extended further to allow systems to construct general rules that explain particular examples in a process called Explanation Based Learning (EBL).

Uncertainty in Rule-based systems

Facts may not be certain, so they may have a certainty factor (In MYCIN from -1 to 1 for degrees of belief of falsity and truth, however, in other systems from 0 to 1) associated with them. So may the conclusions of rules.
Rule based systems are claimed to be modular, with each rule being a module, and because the chain of reasoning can be followed, they allow an easy explanation of their results. However, as the number of rules increase these systems become increasingly inefficient unless rules can be organised into more efficient structures, this increases the complexity of maintaining the system. Finally, static knowledge about objects cannot be easily represented explicitly.

The Inference Engine

The inference engine operates on the two types of memory, the long-term memory (the rules and stored facts - if there are any stored facts) and the short-term memory (the work-space).
As mentioned earlier, inferences are performed during a major control cycle. In each cycle, some rules are matched based on the current contents of the work-space, one or more of the matched rules are selected using some method of conflict resolution, and the conclusions of the selected rule(s) are added to the work-space. This process of using the results of rules to match the conditions of rules in a subsequent cycle is a process of rule chaining. Rule chaining allows the system to move from simple information (facts) to useful conclusions based on the system's rules and inference procedure.

Inference processes

Two basic inference processes are:

• forward chaining (data-driven); and
• backward chaining (goal-driven).

Forward chaining takes the initial facts and works towards conclusions. This may be conducted when a new fact is added to our database of facts and we wish to generate its consequences. It is appropriate in situations where new facts are being added and in which irrelevant conclusions due to a lack of directness do not overburden processing (Russell and Norvig 1995).
Backward chaining takes a hypothesis and attempts to see if it is supported by the facts, this often involves finding support for sub-goals.

R1: IF A and B THEN D

R2: IF B THEN C

R2: IF C and D THEN E

Search strategies

Note that in both cases either a breadth-first or depth-first approach can be used. In the case of forward chaining depth-first will result in only one rule being selected at each time, while for backward chaining, the sub-goals of one sub-goal will be resolved before other sub-goals.

Selecting backward or forward chaining

Backward chaining is preferable when there is a large number of possible inputs in relation to goals.
Forward chaining is preferable when there is a large number of goals and a relatively small number of possible inputs.

Alternative Systems

Model-based reasoning is based on a deep understanding of the problem domain, rather than the heuristics (rules of thumb) gained from experience that rule-based systems rely on. These systems represent the full complexity of causes-and-effects rather than using rules which short-cut reasoning processes
Case based reasoning uses solutions for similar problems on new problems. It involves having a case library of past solutions and then determining which of these is most analogous to the current problem, the selected case(s) can then be adapted to deal with the new situation.
Black-board architectures: these allow multiple experts to contribute and use knowledge by allowing a common area for the sharing of data.

The User Interface

Typically, the interfaces to expert systems must support not only the user of the system, but also the knowledge engineer and the expert whose knowledge is being represented. This means the system must have a way for new rules and facts to be entered. Unfortunately, it is difficult to ensure such rules and facts are consistent and the maintenance of large Expert Systems over time can be complicated.
It is also desirable to have an explanation facility, both for the users and the domain expert. For users so they can accept the recommendations of the system and for experts so they can verify the answers provided by the system

Applications of Knowledge-based systems

Uses of knowledge-based systems and the applied technologies:

• Model-based reasoning:
  • Financial diagnostics - model-based reasoning
  • Equipment failure diagnosis - model based
• Case-based reasoning:
  • medical diagnosis
  • auditing
  • claims settlement
• Blackboard architectures
  • Speech recognition (Hearsay III)
  • Tax planning
• Expert-systems:
  • Medical-diagnosis (MYCIN)
  • Hardware configuration
  • Hardware Configuration for Digital Equipment corporation (R1 or Xcon).

Dynamic Programming

The following material is sourced from Cooper and Cooper (1981)
Dynamic programming is based on the principle that every optimal policy consists only of optimal sub-policies. Unlike back-propagation neural networks, dynamic programming approaches are not subject to local minima or maxima, global solutions are found.
Dynamic programming approaches are particularly interesting because in solving a problem, they actually solve a whole class of problems. Therefore the calculated solution has the potential to be reused to answer other questions.
Here is an example of a problem which can be elegantly solved using a dynamic programming approach (Cooper and Cooper 1981):

The problem involves a traveller who must journey from his home city (city 1) to a destination (city 10). There are a variety of paths he may take through various cities, and the cost of each of those paths is indicated by the numbers on the lines connecting cities. Notice that his trip is divided into five stages, and at each stage he has a choice of which city to go to next, however, he cannot (for example) go directly from a city at stage 1 to a city at stage 3.
He now wants to find the route with the lowest cost. One possible solution to this is to list all the possible routes and calculate the costs of those. This would require calculating the costs for 18 different routes of length 4. However, an alternative is to use dynamic programming.
Dynamic programming begins by moving backwards from the destination. It first find the minimum cost path from cities at stage 3 to cities at stage 4:

City	Minimum Cost	Path
8	380	8-10
9	280	9-10

Now if we are at a city in stage 2 we need to find the minimum cost path to city 10. There are three possible stage 2 cities we could be in:

City 5 - in which the possible paths cost are: (210 + 380) = 590 or (230 + 280) = 510

City 6 - in which the possible paths cost are: (350 + 380) = 730 or (380 + 280) = 660

City 7 - in which the possible paths cost are: (290 + 380) = 670 or (400 + 280) = 680

This gives a table of minimum paths as follows:

City	Minimum Cost	Path
5	510	5-9-10
6	660	6-9-10
7	670	7-8-10

Now we calculate the minimum paths from the cities at stage 1. To do this we calculate the minimum distance for each city taking account of the cost of getting to each stage 2 city and then the cost from that stage 2 city to the end:

City 2 - in which the possible paths cost are: (320 + 510) = 830 or (350 + 660) = 1010 or (400 + 670) = 1070

City 3 - in which the possible paths cost are: (350 + 510) = 860 or (280 + 660) = 940 or (410 + 670) = 1080

City 4 - in which the possible paths cost are: (300 + 510) = 810 or (250 + 660) = 910 or (200 + 670) = 870

This leads to the table below:

City	Minimum Cost	Path
2	830	2-5-9-10
3	860	3-5-9-10
4	810	4-5-9-10

Finally we are left with the choice of which city to travel to from city 1 (Stage 0):

(300 + 830) = 1130 or (200 + 860) = 1060 or (350 + 810) = 1160

This makes the minimum cost path: 1-3-5-9-10. Note this now specifies the policy we will follow. The utility of that policy is the cost which is 1060.
Note that as we move back through the stages, each stage uses the results calculated for the previous stage. Note also, that having solved this problem using this dynamic programming approach, no matter which city we are currently in (i.e no matter where we start), we can find the minimum cost path to city 10.
Dynamic programming in this case has associated a value with each state (i.e learned a value function).

Multi-agent Models and Simulation

The final type of simulation we will be looking at here is multi-agent modelling. These are based on ideas of decentralisation that aim to capture decentralised interactions and feedback loops (Resnick 1997). This is closely associated with the modelling concepts developed in the fields of deterministic chaos and complex systems. Rather than focusing on hierarchy, rules and logic, multi-agent systems allow one to explore the impacts of relationships, interdependencies and negotiation (Resnick 1997). Such models often allow explorations of emergent relationships (for example: what causes traffic jams) in complex systems with many interacting autonomous or semi-autonomous agents. Interesting sites that provide tools for such explorations are the Santa Fe Institute's Swarm Modelling site and the simpler Starlogo site provided by MIT.

References:

Hurley, P.J. 1991 A concise introduction to logic. Fourth Edition. Wadsworth.
Cooper, L. and Cooper, M.W. 1981. Introduction to dynamic programming 1st Ed, Pergamon
Klien, M.R and Methlie, L.B. 1995. Knowledge-based Decision Support Systems, 2nd Ed. Wiley.
M. Mitchell Waldrop. 1992. Complexity: the emerging science at the edge of order and chaos, Chapter 5 - Master of the Game . Penguin.
Smith E. E and Medin, D.L. 1981. Categories and Concepts. Cambridge M.A, Harvard University Press.
McDermott, D. 1981. Artificial Intelligence meets Natural Stupidity . Chapter 5, "Mind Design: Philosophy, Psychology, Artificial Intelligence. Ed. Haugeland, J. MIT Press. pp 143-160.
Pidd, M. 2009. Tools for Thinking: Modelling in Management Science. 3rd Ed. Wiley.
Resnick, M. 1997. Turtles, Termites and Traffic Jams: Explorations in Massively Parallel Microworlds. MIT Press.
Turban, E., Aronson, J.E., Liang, T-P and Sharda, R. 2007. Decision Support and Business Intelligence Systems . 8th Edition. Pearson International.

Decision Processes and Support

This blog looks at:

• Decision making processes
• Problems with the decision making phases
• Bias
• Ideology
• Ethics
• Types of support for decision makers
• Plans
• Forecasts (prediction)
• Exploratory Analysis

The decision making process

This process has been described by a number of authors, each with their own varying steps. Simon (1977) describes the following phases:

1. Intelligence Phase - identify the problem
2. Design Phase - identify and evaluate alternative solutions
3. Choice Phase - selection of a solution
4. Implementation - implement the solution (monitor the result?)

Others divide the phases into an greater number of discrete steps, such as Champoux (2006, p 335):

1. Identify problems and solution criteria
2. Develop alternatives
3. Assess alternatives (rate each on a range of criteria?)
4. Choose an alternative (commit to one)
5. Carry out the decision
6. Assess the decision's effects

However, you can see that Simon's and Champoux's processes, are in essence the same. You can use either of these, or any other variant form as you see fit, however, if you say Simon's phases of decision-making most people will understand what you mean without needing to look up the reference. This process is referred to as "rational decision making'. In later blogs I question the validity and usefulness of such a discrete ordered sequence of clearly separated steps/phases and I relate this to modern methods of system's development. For now though I simply highlight some difficulties in each of Simon's phases.

Problems with the decision making phases

Phase 1 Problem identification:

What some people regard as a problem others may regard as entirely natural. For example, a recession may be seen by some as creating a problem of unemployment, however, others may argue that such cycles are an inevitable part of a free-market system and that these periods are to be accepted and endured. This occurred to some extent with the Global Financial Crisis in 2008. Some economists argued that it was difficult to detect a bubble, as if housing prices increase maybe wage increases will follow? (although in Australia in 2008 house prices had increased for many years without commensurate wage increases). Some discussion of this issue is available here.

Phase 2: Design a solution (generate and evaluate alternatives):

This requires collecting information. Often not enough information is available as needed. Furthermore, available information may be incomplete or biased, ignoring some data. This can lead to pre-determined, desired outcomes for vested interests (more on this later). The following hypothetical article on a US steel company by Gregg (1936) describes this point:

“Our great executive organizations,—f inancial, manufacturing, commercial, and governmental,—are so large that it is impossible for their chief executive officers to know the full truth about what is happening to the people in them. If, for instance, there is trouble in a little Minnesota iron mining town controlled by the U. S. Steel Corporation, the President of the Corporation cannot find the essential truth about it. He has not time to go there and look into it in person, and even if he did go, the people there would be so overawed by his position that they would be afraid to tell them all the details. If, instead, he writes to the local representative of the Steel Corporation in that town, that man, even assuming he is honest and fair-minded, will not tell the whole truth. He will not mention his own mistakes; he will not adversely criticise or “let down” his immediate subordinates because he relies on them to do what he thinks necessary. Nor will he criticise his superiors, because he depends on their favor for his job. All three of those people,—himself, his immediate subordinates and his superiors— are human and have therefore made mistakes. But none of those three sets of mistakes are going to get into that man's report to the President. Yet the President has to make an executive order based on that man's report. Hence it is practically impossible to have that order really just, because the facts needed for it cannot be obtained by the President who has to make it. When you add, as you must, an allowance for the average amount of selfishness, prejudice, pride, ignorance, stupidity, lack of imagination, ambition, jealousy, greed, and dishonesty, both in relation to the making of that report, the consideration of it by the President's assistants and advisers, and the administration of the President's order after it is made, the probabilities of injustice to the rank and file of workers and people on the periphery of that immense organization are greatly increased. Indeed, there is sure to be great and constant misunderstanding, injustice and consequent resentment and friction. That is true of all large executive organizations, no matter what their field of action. The larger they are the more certainly does this condition exist. Their very size makes them humanly inefficient, whether or not they are mechanically or financially efficient. Such a result is a matter of psychological necessity.”

De Bono (2005) argues that this phase needs more attention, as people do not generate enough alternatives. He argues that more creativity and lateral thinking is required before moving on to choose from the available alternatives.

Phase 3: Selection from alternatives (the decision)

Others argue that it is on the selection phase that people must concentrate. In a speech by Alan Greenspan, Chairman of the US Federal Reserve testifying to congress at the end of 2008 about what had caused the financial crisis (Patel, 2009) one panel member questioned Greenspan in relation to a previous statement by Greenspan about free markets (which I will examine in the next section):

WAXMAN: ... Do you feel that your ideology pushed you to make decisions that you wish you had not made?

GREENSPAN: Well, remember though, what an ideology is. Its a conceptual framework with [sic] the way people deal with reality. Everyone has one. You have to. To exist you need an ideology. The question is whether it is accurate or not. What I am saying to you is, yes, I found the flaw. I don't know how significant or permanent it is, but I have been very distressed by that fact.

(some text omitted)

WAXMAN: In other words, you found that your view of the world, your ideology, was not right, it was not working?

GREENSPAN: Precisely. That is precisely the reason I was shocked, because I had been going for 40 years or more with considerable evidence it was working exceptionally well.

Patel (2009) claims that Greenspan's ideology "warped his view about how the world was organised" and lead to poor decisions (i.e a poor selection from the possible alternatives).

Stage 4: Implementation – need buy in from various stakeholders.

In the implementation stage we are interested in both acting so as to implement the decision, but also in evaulating the effects and outcomes of our decision, ideally so that we can learn from past mistakes. When implementing decisions organisations should behave in a lawful way. However, in the documentary "The Corporation", the behaviour of organisations is examined by the film makers. They find that corporations often behave in ways that are consistent with psychopathic behaviour in humans, including (Patel, 2009):

1. Failure to conform to social norms with respect to lawful behaviours as indicated by performing acts that are grounds for arrest.
2. Deceitfulness, as indicated by repeated lying, use of aliases or conning others for personal profit or pleasure.
3. Impulsivity or failure to plan ahead
4. Irritability and aggressiveness, as indicated by repeated physical fights or assaults.
5. Reckless disregard for safety of self or others
6. Consistent irresponsibility, as indicated by repeated failures to sustain consistent work behaviour or honour financial obligations
7. Lack of remose, as indicated by being indifferent to or rationalising having hurt, mistreated, or stolen from another.

Negative impacts on others should be considered in our evaluation of our decision outcomes. Some authors, such as Naomi Klein (The Shock Doctrine) have documented the negative effects of various organisations on external parties. This raises obvious issues of ethics, but also of long term sustainability. One aspect of this problem is negativ e externalities. Negative externalities are costs of production paid by the public/society in terms of repairing environmental damage, paying for road systems to support industry as well as various subsidies. In one study it was estimated that for every private dollar spent on pesticides 80 cents of public money was spent to clean up their effects (Patel 2009).

One aspect of this problem seems to be that even if individual decision makers do want to act in a way that is ethical and sustainable (and reduce externalities), their business is punished by the market place. Patel (2009) cites Jan Kees Vis, Director of sustainable agriculture at Unilever (a company renowned for its environmental and sustainability leadership) as being caught in this dilemma. Patel (2009) argues that due to the hidden costs much food available at supermarkets that appears to be cheap food is in fact cheat food (as in passing some costs on to others).

The hidden costs of free goods are evident in other ways. Newspapers, for example, are now struggling to stay profitable with their bread-and-butter revenue of classifieds now moving to specialised online advertising companies (eg: carsales.com.au, domain.com.au). With more and more people reading papers for free online newspapers have less money for quality journalism. This is an effect which threatens the role of the media as a community watch dog over other democratic and legal systems (Patel 2009).

It is due to concerns about the above issues, among others, that triple-bottom-line accounting practices have been proposed. These suggest that business organisations focus not just on the traditional bottom line of profit, but also account for their organisation's effects on society and the environment (Lamberton, 2005). Triple-bottom-line accounting should support a broader consideration of the impacts of corporate decision making.

Types of Support for decision makers

This section is based on Schumacher (1973).

Decisions are typically made with some intended outcome or goal. Deciding on an appropriate goal is a significant problem in itself and one we will deal with separately later. But given that we have a goal, making a decision implies that that we expect that decision to lead us to, or closer to, that goal. But on what do we base that expectation? And how reliable is that basis? Clearly some things are reliably predictable, some things unreliably predictable and others entirely unpredictable. In choosing what you want to base your decisions on your first task is to determine which of these situations you are dealing with. If you believe something is predictable you need convincing reasons as to why you believe that and under what circumstances you may be proved wrong. Establishing the type of problem and environment you are dealing with will allow you to determine an appropriate basis for the decision. Three possible bases include (there are others): forecasts (predictions), plans and exploratory calculations.

Forecasts/Predictions

Forecasts (predictions) include the uncertainty of events in the real world, but not your acts in relation to those events. Eg: Will a trend of growth continue, flatten off or decline? When dealing with people and freedom of action prediction becomes difficult. Most of the time most people act fairly mechanically, however, a tiny minority can generate significant change and innovation which can disrupt these patterns (see also: Christensen, Horn and Johnson 2008). It might be possible in some cases to create sophisticated computer programs to apply mathematical pattern processing, however, the more sophistication needed to detect and extrapolate a pattern the more likely that pattern is a weak and obscure basis for extrapolation in real life.

Plans

Plans are a statement of intention. Plans can be certain in regard to what you are going to do (your acts are certain given a situation), but uncertain in relation to what might occur during the execution of your plan (events outside your control). In this sense they may include forecasts.

Exploratory calculations

Also known as feasibility studies. These are conditional statements which are presented as a certainty given that the conditions are true. “What if?” questions and extrapolations fall into this category unless they include many possible outcomes based on probabilities. Eg: “If such and such a trend of events continues for another x years, this is where it would take us”. Note this is not a forecast (prediction) as it is conditional and does not take into account uncertainty.

Note that forecasting is fraught with danger. The following provides a recent example of how forecasting models may fail. It is a speech by Alan Greenspan, Chairman of the US Federal Reserve testifying to congress at the end of 2008 about what had caused the financial crisis (Patel, 2009):

"a Nobel Prize was awarded for the discovery of the pricing model that underpins much of the advance in derivatives markets. This modern risk management paradigm held sway for decades. The whole intellectual edifice, however, collapsed in the summer of last year because the data inputed into the risk management models generally covered only the past two decades, a period of euphoria. Had instead the models been fitted more appropriately to historic periods of stress, capital requirements would have been much higher and the financial world would be in far better shape today, in my judgment."

According to Patel (2009) the model had faulty assumptions (based on limited data from recent decades) and so the output was wrong. He describes this as a classic case of Garbage-in-Garbage-Out (GIGO). The parameters were wrong so its outputs were wrong.

Measuring outcomes and risks

While we do not deal with determining goals here as a final point in this section we will raise some issues regarding outcomes of decisions. One issue that is of interest in particular is the effect on stakeholders. A stakeholder is anyone with an interest in what the organisation is doing (Jackson, 2003). Effects on, and the involvement of, stakeholders is an issue that we will continuously consider in later material. However, some issues that are worth considering in relation to setting goals is that decision makers tend to prefer quantifiable outcomes (measurable) however, Schumacher (1973) argues that we should not ignore qualitative aspects such as health, beauty, cleanliness. He asks the question of how these aspects can be valued in decision making. It is claimed that cost/benefit analysis can accommodate these types of values however Schumacher believes that in fact cost-benefit analysis is just a procedure whereby the priceless is given a price leading to self-deception or the deception of others; an elaborate method of moving from preconceived notions to foregone conclusions.  George Monbiot discusses exactly this in his article An answer to the meaning of life  in which he discusses how assigning values, which are essentially arbitrary, to nature allows developers to effectively provide economic rationales to justify the destruction of nature. To quote:

“Picture, for example, a planning enquiry for an opencast coal mine. The public benefits arising from the forests and meadows it will destroy have been costed at £1m per year. The income from opening the mine will be £10m per year. No further argument needs to be made. The coal mine’s barrister, presenting these figures to the enquiry, has an indefeasible case: public objections have already been addressed by the pricing exercise; there is nothing more to be discussed”

The cost-benefit nearly always “comes out right” for business.

References

Champoux, J.E. 2006. Organisational Behaviour: Integrating Individuals, Groups and Organisations. 3rd Edition. Thomson.

Christensen, C.M, Horn, M.B and Johnson, C.W, 2008, Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns, Mcgraw-Hill.

De Bono, E. 2005 De Bono's Thinking Course. BBC Books.

Gregg, R.B. 1936 The value of voluntary simplicity. Pendle Hill.

Jackson, C. 2003 Systems Thinking: Creative Holism for Managers, Wiley.

Lamberton, G. 2005. 'Sustainability accounting—a brief  history and conceptual framework', Accounting Forum, vol. 29 no. 1 March. Available here.

Patel, R. 2009. The Value of Nothing. Black Inc.

Minzberg, H. 1989 Mintzberg on Management: Inside Our Strange World of Organisations. Free Press.

Monbiot, G. 2011, An Answer To The Meaning of Life, Viewed June 30, 2011

Schumacher, E.F. 1973 Small is beautiful: a study of economics as if people mattered, London. Blond and Briggs.

Simon, H. 1977 The New Science of Management Decision. Englewood Cliffs, NK. Prentice Hall.

Ways of Managing and Making Decisions and the role of MIS

This blog looks at the:

• Styles and Processes of Leadership and Decision Making
• Autocratic
• Democratic
• Organisational Forms
• Bureaucracy
• Adhocracy
• Examples of Organisational Management
• Scott Bader
• GE
• Semco 

Styles and Processes of Management

In this section I look at styles of leadership, in particular autocratic and democratic styles.

Autocratic Leadership and Decision Making

Daft and Pirola-Merlo (2009) describe an autocratic leader (boss-centred) as one who tends to centralise authority and derives power from position, control of rewards and coercion. They argue that an autocratic approach is appropriate where there are time constraints or if sub-ordinates are too low-skilled to participate in the decision making process. They cite fast-food restaurants as an example, however, we believe it is possible that the autocratic approach in these business is more due to the mass production techniques (they refer to it as fast turnover) used in which the environment is so controlled that there is in fact little opportunity for decision making or any other form of creative thought. We will examine how methods of organising work may affect decision making later in this lesson. However, our case is consistent with that of Schumacher (1980) who argues that regardless of the level of democracy in our society's political systems methods of management tend to be autocratic. Although he is not convinced that if workers were given more control over their circumstances (as we will see some of examples later) that most would do things very differently. This is because, he argues, they, like everyone, have been conditioned by our modern industrial society to accept its values and criteria. Despite this he believes that autocratic management is undesirable from a human perspective as it does not allow sufficiently for satisfying work in ways that respect human dignity. In relation to this, Daft and Pirola-Merlo (2009) cite studies in which it was found that subordinates with autocratic leaders performed well as long as the leader was present to supervise them, however, subordinates disliked the autocratic style of leadership and felt hostility towards their managers.

In Buck and Villines (2007), John Buck describes his own feelings of working in a large bureaucratic organisation: "Nothing overtly bad happened. I simply felt powerless, and something deep inside of me shredded" (p 19). He attributes these feelings to the lack of equality (in relation to decision making) - which he refers to as "disenfranchisement" - in the work-place - which he describes as a feudal system in which most people are servants, not that different to medieval serfs. This theme is developed further by Schmidt (2000). In relation to non-professional workers Schmidt (2000) states: "Nonprofessionals often feel that their employers treat them like unthinking machines, and they long for the more human treatment that they see their professional co-workers receiving ... in the professional/nonprofessional division of labour, nonprofessionals play a role analogous to that of a machine." (pg 38). In relation to professionals Schmidt (2000) claims that they are allowed autonomy on the basis that their decisions will support the ideology of their employers: "their work involves judging whether or not the ideas of others are in line with the favoured outlook" (pg 41). Furthermore, he claims "Professionals generally avoid the risk inherent in real critical thinking ... They are simply ideological thinkers ... [who] give the appearance of being critical thinkers as they go around deftly applying the official ideology and confidently reporting their judgments" (pg 41).  According to Schmidt professionals are acutely aware of underlying issues and politics, whereas nonprofessionals "are often oblivious to the forces that are contending beneath the surface of the work, and so their decisions may advance the wrong interests - wrong from their employers' point of view" (pg 42). This, according to Schmidt, is the reason why only trusted professionals are allowed autonomy. For complex and creative work, employers cannot tell people exactly what to do (which is more often feasible with unprofessional work), so when it comes to roles that require decision making they need employees that can be trusted to make decisions that will be in the employer's interest. Since such work cannot be directly and constantly supervised this requires people who can "make decisions that must be made ideologically" (pg 38). Schmidt (2000, pg 37) argues that a lot of professional training is, in fact, indoctrination into this ideology. Such highly trained workers are expensive, according to Schmidt, so this drives employers to try and reduce the discretion of professionals by either standardising the work procedure or introducing "'expert' computer systems" (pg 38) the intention of which is to "transform the employee's decision making into a routine or rote activity and tend to strip the work-result of any imprint of the employee's own thinking" (pg 36). 

Democratic Leadership and Decision Making

Daft and Pirola-Merlo (2009) describe a democratic leader as one who delegates authority to others, encourages participation, relies on subordinates' knowledge for completion of tasks and depends on subordinate respect for influence (authority?). The studies cited indicated that subordinates with democratic leaders performed well even when the leader was absent and that they had positive feelings towards their managers. In these studies participative decision making approaches were used, such as majority rule. Other studies suggest that managers'/leaders' behaviour may range from autocratic to democratic depending on circumstances. Daft and Pirola-Merlo (2009) cite a case-study at ICI (now Orica) where participative decision-making was practiced such that members of a technical project team were involved in many key decisions. The team leader, Lisa Madigan, argued that this approach allowed them to solve problems that might not have been dealt with well by conventional problem-solving techniques. Interestingly she stated that this approach allowed them to get solutions quickly. One of the arguments made by Draft and Pirola-Merlo (2009) for an autocratic style was that it was more appropriate when decisions needed to be made quickly, now we hear that a participative approach can be fast. How can this be explained? One possible answer is that participative/democratic approaches need not be majority-rule (which can be slow) but rather a form of distributed problem solving whereby decisions are delegated to those best able to make them. This may explain the faster, and better quality decisions experienced in Madigan's team at ICI. Such an effect is, according to Galbraith (1985) the basis of the success and efficiency of bureaucracies. We will discuss these ideas of Galbraith in a later lesson.

Organisational Forms

Mintzberg (1979) described various forms of organisational structure. These include a simple structure of direct supervision over an organically arranged operational core through various types of bureaucratic organisation to adhocracies (among others). I will look at bureaucracies, divisions and adhocracies in the next section and other organisational forms in later blogs.

Bureaucracy

Two types of bureaucracy identified by Mintzberg (1975) are the Machine Bureaucracy and the Professional Bureaucracy. The Machine Bureaucracy (MB) may be found in large organisations such as post-offices, airlines and prisons. It is suited to simple and stable environments and is based around standardising work processes. In this structure formal power rests primarily with the managers at the apex of the system, however, informal power also rests with the technical experts who are responsible for standardising everyone else's work.

According to Mintzberg, in a Professional Bureaucracy rather than standardising work, skills are standardised. Note that in both cases, standardisation allows decentralisation as this leads to predetermined and/or predictable behaviour. Standardisation of skills is a coordinating mechanism that allows for both standardisation and decentralisation at the same time. It is found in academic institutions such as schools and universities as well as in hospitals. The Professional Bureaucracy relies on trained and indoctrinated specialists. Due to this preparation these specialists can be left to work autonomously in accordance with their training. For example, teachers are left alone to work with their students in classes, doctors are left alone to work with their patients. Both are largely unsupervised. This independence of colleagues relies on everyone knowing what others are doing and how the system works due to the standard training. One feature of these bureaucracies is that the work processes are often too complex to be standardised directly by analysts as in the Machine Bureaucracy. For example, imagine trying to program a teacher in their work in the classroom. A lot of power rests with the professionals working in these systems. The professional is answerable only to his colleagues who, as a group, trained and indoctrinated him/her and therefore can censure him/her for malpractice. This power also comes from the fact that the work is too specialised and complex to be supervised by managers and also because of this, they have professional mobility due to high demand for their services (Is this true? See the Activities for this lesson). Loyalties lie with professional associations, not organisations. Careers depend on professional progress, not climbing an administrative ladder. Professionals join organisations to share resources, to obtain clients and to allow joint servicing of clients (i.e some diagnose problem, some prescribe remedies). In universities academics join together to offer courses.

The Divisional Form

Whereas the Professional Bureaucracy is an integration of autonomous individuals, the Divisionalised Form (DF) is an integration of autonomous business units with a central administration (the headquarters). It was used in the socialist economy where state enterprises were divisions and the central government agencies were the headquarters. This form is common in large companies and often sits as a superstructure on top of other organisational forms as each division may have its own internal structure. Divisions may be formed according to different markets/products. These divisions can then operate free of the need to co-ordinate with the other divisions. Again it is a structure that allows decentralisation and autonomous leadership with each division delegated the power to make its own decisions regarding its operations. In these structures power often mainly resides with the divisional head. However, the headquarters needs to retain some power over the division or the division becomes an independent organisation. Headquarters typically do this by allocating overall financial resources and monitoring the results of decisions made in the divisions. This monitoring is typically based on quantitative measures of profit, sales growth and return on investment (ROI). Monitoring such results frees the headquarters from monitoring the processes used to achieve them. The coordinating mechanism here is therefore the standardisation of outputs. Because of its dependence on standard performance outputs this form of co-ordination is difficult in dynamic environments, but appropriate to stable environments. Often the divisionalised form is composed of many machine bureaucracies.

One form of power that the headquarters has is the ability to design the performance control system. This involves designing the performance metrics and the reporting periods, establishing formats for plans, budgets and reports then designing a Management Information System (MIS) to feed the performance results to the headquarters in conjunction with setting targets and reviewing the MIS results. If results are not up to expectations the headquarters (HQ) can decide that this is due to factors outside of the divisional manager's control (eg: recession, new competitors, etc) or they can replace the division manager if he/she is perceived as incompetent. Such new appointments are the most direct way HQ can interfere with the operation of the divisions. The MIS may offer little assistance in determining whether problems are due to incompetence or adverse business conditions, also the MIS results may be manipulated by the divisional manager (eg: by cutting spending to gain short term profitability at the expense of long term profits) or may not reveal imminent problems. To overcome such problems of the MIS, headquarters may monitor divisional performance on a personal basis including frequent visits to the division to collect information ("to keep in touch"). Headquarters managers may be tempted to take over divisional powers and centralise decision making (eg: one advertising department instead of many). Often this decision may be based on the abilities of MISs to supply the necessary knowledge. However, Mintzberg (1979) warns that such systems can give the illusion of knowledge but not knowledge itself of which much is soft and speculative and is never quantified or documented. The MIS only provides, typically, abstracted and aggregated generalisations. If the MIS is capable of providing more detailed information, or if central mangers try and use phones to get it verbally, they would lack the time to absorb it all. The time needed to absorb the information was the main reason why organisations were divisionalised in the first place. Headquarters should not try to manage divisions but to set targets and monitor performance using an MIS. The use of a headquarters in a divisional form also overcomes the problem of top management deceiving its directors.

A major criticism of the divisionalised form is that it may drive divisional managers and their divisions to act socially irresponsibly because the metrics used by HQ (measures and performance targets) are typically economic. Furthermore, social outcomes are often intangible and therefore difficult to quantify and manage using an MIS.

Adhocracy

The bureaucratic and divisional forms are best suited for established practices in simple and stable environments. They are not well suited to change, although the divisional form does allow for some degree of incremental change. However, for innovation and rapid change in complex environments a more flexible structure is needed. One name for such a structure is an adhocracy. Adhocracies have organic (adaptive) structures that do not have highly formalised behaviours. These are co-ordinated based on mutual adjustment and decentralisation is selective based on the required mixtures of staff needed at various places and times. Coordination therefore arises not from control but through interaction. Innovative organisations do not rely on any form of standardisation for co-ordination but rather are co-ordinated around projects. This form of organisation may involve vast bureaucracies being repeatedly reformed over short time periods with divisions being created and destroyed with jobs and responsibilities being frequently transformed. They are appropriate to organisations that are not involved in repetitive work such as new space projects and film projects.

Examples of Organisational Management

Schumacher (1980) describes the organisational structure at Scott Bader that Ernest Bader started implementing in 1951 and is still operating now. During this time the company grew substantially. The structure was implemented when Ernest Bader, who owned the large chemical company Scott Bader, which he founded, decided to set up the organisation as a commonwealth for all who worked in it. In doing this he transferred all his equity in the company to the commonwealth, which is owned by all who have worked for the company for a minimum time i.e private ownership has been abolished. Baden then had to address the issues of how to arrange for participation in the commonwealth and how to protect workers during difficult times from being excluded from the commonwealth (i.e losing their jobs). Rather than having a board of directors the company instituted a parliament of workers. This parliament could appoint or dismiss directors and determined their salaries. They also settled on a maximum spread of income between the highest and lowest paid. No one could earn more than 7 times the lowest paid worker in the company (compare this with Telstra where Sol Trujillo earned more than $30 million over 3 years). Employees could also not be sacked except in cases of gross personal misconduct. Information on other employee-owned companies can be obtained from the Employee Ownership Association. At Scott-Bader originally the number of employees was limited to 400, however, it is now 600. Once the company grows too big, sections are split off as separate companies. Only 40% of the profits can be distributed (the rest is reinvested) and of this half must go to charity or social purposes outside the organisation. After the first 30 years of operation the organisation was struggling to find charity organisations that needed help within 50 miles as all other social needs seemed to have been met by prior Scott-Bader contributions (read more about the Scott-Bader organisation here).

Two other interesting example which may be worth exploring are Semco in which much control of the organisation was given to workers and also the experience of GE when it implemented Management By Objectives (MBO) where each manager sets his or her own goals with an emphasis on self control rather than imposed control (Champoux 2006 pp 19-21).

References

Champoux, J.E. 2006. Organisational Behaviour: Integrating Individuals, Groups and Organisations. 3rd Edition. Thomson.

Daft, R.L and Pirola-Merlo, A. 2009. The Leadership Experience, Asia Pacific Edition, Cengage Learning.

Galbraith, J.K. 1985. The New Industrial State, Chapter VI. Houghton Mifflin, Boston.

Mintzberg, H, 1975. The Structure of Organisations. Prentice-Hall.

Schumacher, E.F, 1980, Good Work, Abacus.

Schmidt, J. 2000. Disciplined Minds: A Critical Look at Salaried Professionals and the Soul-Battering System that Shapes their Lives, Rowman and Littlefield.

The Purpose and Role of Management

This Blog looks at the:

• Purpose and role of organisations
• Purpose and role of management

Purpose and Role of Organisations

The very name organisation implies that they help organise aspects of society, but what role organisations play depends on what purposes you are interested in and also on what types of organisation you are talking about. Economists concerned with the management and allocation of scarce resources might describe corporate organisations as serving an allocation purpose (although the reasoning of Vance(1963) might suggest that in Western nations resources may not have been very scarce in recent decades). Another view is that the purpose of organisations is to produce products or services (Wood et al 2001). Lawyers and accountants might regard the role and purpose of corporate organisations as producing a profit (Section 181 of the Corporations Act). However, there are of course many non-profit organisations and the needs of these, both large and small, also need to be considered in terms of their support requirements.

Purpose and Role of Management

According to Haynes and Massie (1961) management involves "doing the job through people" (p. 12). This is one perspective on the role of the manager and its people focus is one which we will raise again later in this lesson and in subsequent lessons. Champoux (2006) distinguishes between managers and leaders. He argues that they play different roles in organisations. Managers sustain and control organisations; leaders try and change them. This view is common and Richard Branson is perhaps an example, he creates the business then leaves its management to others while going on to his next business creation project (thus Virgin Records, Virgin Airways and Virgin Mobile).

According to Champoux (2006) leaders are visionary risk takers who seek opposing views and do not use coercive methods, but may introduce conflict and chaos. On the other hand, managers follow an existing vision; they solve problems and bring order to the workplace. They take few risks and use both rewards and punishments to achieve predictable behaviour. Champoux argues that leaders are good change agents suited to fast changing external environments, while managers are suited to stable environments. Champoux takes a very modern view of organisations, of leadership and management. Traditionally, the management role has been thought about as a set of arbitrary functions such as: decision-making, organising, planning, directing, controlling, staffing, co-ordinating, communicating, motivating and evaluating (Haynes and Massie 1961). Information obviously plays a key role in all of these functions, whether it is information on how current directions are going, or where motivation may be needed. In theory, a variety of information systems could be used to collect information and to support all of these. In each case, from an information systems perspective we would be concerned with:

• What information can and should be collected?
• From whom and where?
• For what purpose?
• How should it be stored and accessed?
• What are the privacy and security issues associated with its collection, storage and use?
• How reliable is the information?

These are all questions which I am interested in addressing.

However my blogs are structured not around these issues, but rather around another, single, issue. That issue is: How is the information used? Any use of the information must involve some action (or inaction) which in turn requires a decision about what action should be taken. Actions are taken to achieve particular purposes, and different purposes require different information so we can see that thinking about management from a decision making perspective leads us back to our information systems list. Thinking about management as decision making also encompasses all functions of management and styles of management, whether that style be one of chaotic leadership or stable management.

Therefore, it is from the perspective of decision making that I approach this topic. However, in doing this it is important to consider the relationship between theory and practice. Particularly in complex systems, such as those involving people. Mintzberg (1989) who studied managers going about their daily work commented that when asked what they do, managers will probably tell you that they plan, organise, coordinate and control. However, he suggests that if you actually watch what they do "Don't be surprised if you can't relate what you see to those four words" (pg 9). Throughout these blogs I will be considering how ideas might in fact be implemented and whether they are generic abstractions, creations of pure thought to allow us to talk about issues, or relevant to actual real world scenarios. This is called dialectical thinking. Brookfield (2000, p 90) provides the following statement about thinking dialectically in relation to decision making:

"universal rules, general moral strictures and broad patterns of causal and prescriptive reasoning ('if this is the case then I should do that') is balanced against, and constantly intersects with, the contextual imperatives of a situation ... the recognition that specific situations make nonsense of general rules and theories".

Another take on the role of managers relates to the nature of the work itself. Parkinson's Law is the observation that "work expands to fill the time available" (Haynes and Massie 1961, pg 29). The Haynes and Massie (1961) reprint of Parkinson's article (pg 29) (which is also available here) describes a hypothetical situation in which a manager perceives that he is overburdened and appoints two new subordinates which ultimately result in him becoming even busier. The Economist (1955) (available here) also describes the real world situation in which the number of British Navy officials nearly doubled between 1914 and 1928 even though over this time the number of officers and men available for fighting decreased by around 30 percent and the number of commissioned vessels dropped by two thirds. This was said to have resulted in a "magnificent Navy on Land".

Minztberg (1989) notes that in his, and in others’, studies of management CEO's and managers interact with a wide range of people everyday including: subordinates, clients, business associates, suppliers, managers of similar organisations, government and trade organisation officials, fellow directors on outside boards, and so on. He explains that managers cultivate such networks to find information, building in effect the manager's own external, and effective, information system.

All the above articles together suggest that management is a role that involves a range of complexities and that it is one that is still not fully understood. In this unit in addition to the work of managers themselves, we will also be interested in the relationship of their role to the surrounding society and various stakeholders. In particular, we are interested in the ethical considerations of management and also issues related to changing social phenomena such as triple bottom line accounting.

References:

Brookfield, S. 2008 'Adult cognition as a dimension of lifelong learning'. In: Field J. and Leicester, M. (eds) 2008 Lifelong Learning: Education across the lifespan. Routledge.

Champoux, J.E. 2006. Organisational Behaviour: Integrating Individuals, Groups and Organisations. 3rd Edition. Thomson.

Haynes, W.W and Massie, J.L, 1961, Management: analysis, concepts and cases. Prentice-Hall.

Mintzberg, H. 1989 Mintzberg on Management: Inside Our Strange World of Organisations. The Free Press. N.Y.

Polanyi, K. 1944 The Great Transformation: The Political and Economic Origins of Our Time . Boston: Beacon Press by arrangement with Rinehart & Company, Inc.

Taylor, J. and Moosa, I. 2002. Macro economics 2. 2nd Edition. Wiley.

The Economist, (1955) Parkinson's Law, November 15. Reprint available online at: http://www.economist.com/business-finance/management/displaystory.cfm?story_id=14116121

Vance, P. (1963) The Waste Makers. Penguin.