The Proverbs of Administration by Herbert Simon — A Summary

Herbert A. Simon, “The Proverbs of Administration,” Public Administration Review 6, no. 1 (1946): 53–67.

Such a badass title!

Proverbs are useful for persuasion especially when used retrospectively. One can always find a proverb to prove one’s point — or the opposite point for that matter. But when they are used in scientific theories, they are less useful and more harmful. Given their very nature they can both prove or disprove anything. If Newton had announced that all matter both attract and repulse each other, he would not have contributed anything useful. But sadly, most of the propositions of administrative theory today possess that property of proverbs. The paper will substantiate this sweeping criticism.

Some Accepted Administrative Principles

Among the common accepted “principles” of administration are:


Administrative efficiency is increased by a specialization of the task among the group.

But does any increase in specialisation lead to increase in efficiency?

Consider two plans of nursing the first of which requires nurses to specialise by place — nurses are assigned to districts and do all the work in that district — and the second of which requires nurses to specialise by function — nurses are assigned to specific functions, TB nursing for example, which they perform in multiple districts.

The proverb of specialisation is useless in helping decide which of these alternatives should be chosen. As it turns out, specialisation is not a condition of efficiency but is the inevitable result of all group activity for the simple reason that a person cannot be doing two different things at the same time.

Unity of Command

Administrative efficiency is increased by arranging the members of the group in a determinate hierarchy of authority.

This proverb requires that a subordinate should not have multiple superiors from whom he receives orders. This is clear enough.

However, if unity of command is observed strictly, there will be inefficiency in situations that require multiple forms of specialised expertise. For example, should the accountant in a school department who is subordinate to an educator never listen to the orders of the finance department regarding the technical aspects of his work? Of course, some irresponsibility and confusion will ensue if unity of command is not followed. What is needed is a principle that helps weigh the advantages and disadvantages of both courses of action.

Span of Control

Administrative efficiency is increased by limiting the span of control, at any point in the hierarchy to a small number.

But administrative efficiency is also enhanced by keeping at a minimum the number of organizational levels through which a matter must pass before it is acted upon. This equally plausible proverb contradicts the other proverb.

In large organisations, restricting the span of control inevitably creates excessive red tape as more levels are added to the organisational structure. But increasing the span of control beyond a certain point will weaken the authority of the supervisor. Where then lies the appropriate span of control lie? The proverbs are useless again in providing an answer to this critical question.

Organization by Purpose, Process, Clientele, Place

Administrative efficiency is increased by grouping the workers, for purposes of control, according to (a) purpose, (b) process, (c) clientele, or (d) place.

As is clear from the discussion on specialisation, these purposes of control are contradictory and the achievement of the first kind of specialisation can come only at the cost of the other three. It is also naïve to see the kinds of specialisation as separable. On examination, it will be found that the difference between “process” and “purpose” is only one of degree. Purposes are generally arranged in a hierarchy and the purpose of one process may be the process for another higher purpose and so on. Consider a typist who moves his fingers in order to type; types in order to reproduce a letter; reproduces a letter in order that an inquiry may be answered. “Clientele” and “place” are part of purpose, not apart from it. Any complete statement of purpose will have to specify “place” which integrates “clientele” with it. The purpose of a fire department, for instance, would have to include the area (the place) served by it which would necessarily include the people living in the area (the clientele).

It is therefore not legitimate to speak of a “purpose” organization, a “process” organization, a “clientele” organization, or an “area” organization. The same unit may be any of these depending on the nature of the larger organisational unit where it is located. It is correct only to say a certain bureau is a process bureau within a certain department. Even when the ambiguities with the usage of the terms are clarified, the “principles” of administration, needless to say, give no guide as to determining which of the competing bases of specialisation is applicable.

The Impasse of Administrative Theory

The problem with the “principles” is that they are treated as such when they are actually only criteria for describing and diagnosing administrative situations. Closet space is an important criteria for the design of a house but a design made on the principle of having maximum closet space will be quite unbalanced.

In administration, it is necessary that “all the relevant diagnostic criteria be identified; that each administrative situation be analysed in terms of the entire set of criteria; and that research be instituted to determine how weights can be assigned to the several criteria when they are, as they usually will be, mutually incompatible”.

The Description of Administrative Situations

Just as the concepts of “acceleration” and “weight” were developed before a law of gravitation could be intelligibly formulated, administrative theory needs to develop operational concepts — that is, terms whose meanings correspond to empirically observable facts or situations — before it can recommend sweeping principles.

Most descriptions of organisations in administrative theory fall short of scientific standards by confining themselves to “allocation of functions and the formal structure of authority”. A description of the functions — generally, that a bureau performs this function while another performs that function — provides little to no information about the manner in which the organisations work.

“Administrative description suffers currently from superficiality, oversimplification, lack of realism.” Until it undertakes the tiresome task of studying actual allocation of decision-making functions, there is little hope for rapid progress towards identifying and verifying valid administrative principles.

A purely formal description of administrative organisation might be impossible for the simple reason that real-world content plays a greater role in the application of administrative principles than formal precepts.

The Diagnosis of Administrative Situation

Propositions of administrative theory are concerned with the “principle of efficiency” — that is, the greatest accomplishment of administrative objectives for a given level of expenditure or the minimum expenditure of resources for achieving a given objective.

But the “principle” of efficiency should be considered not as such but only as a definition because it does not tell how the accomplishments are to be achieved but only that maximisation is the aim of administrative activity.

How to attain the level of efficiency or maximise the attainment of administrative objectives? Consider a single member of the organisation and see what the qualitative and quantitative limits to his output are. He may be limited by skills, habits, and reflexes that are not in his consciousness — for instance, manual dexterity, strength or reaction time. He may further be limited in his decisions by his values and his conceptions of what the purpose of the organisation is — his greater loyalty to the bureau may compel him to make decisions that are inimical to the larger organisation. He may also be limited by the extent of his knowledge of things relevant to his job. The first is a limit on his ability to perform and the other two are limits on his ability to make rational decisions. There may be other limits too but the point is that administrative theory must consider such limits as are present and come up with valid and non-contradictory principles. Only the first, thanks to the Scientific Management of Frederick Taylor, has been satisfactorily examined.

The limits of rationality are variable and may be influenced by consciousness of that very limitation. Rationality makes sense only when seen in terms of the larger objectives of the organisation and not the specific objectives of the individual administrator. Also, administrative theory is concerned with the non-rational limits to rationality. The greater the rationality, the lesser the importance of the exact form of organisation.

Assigning Weights to the Criteria

First, an operational (see under The Description of Administrative Situations) vocabulary for describing administrative organisations must be developed. Second, the limits of rationality in making decisions must be studied to understand the criteria that have to be weighed in evaluating an administrative organization.

It is not enough to identify the criteria (that the span of control must be decreased). But it is more important to weigh its benefits with the possible adverse effects it might bring about (how adversely will reducing the span of control affect the culture of contact between higher and lower ranks of the hierarchy?). This can only be possible through empirical research and experimentation.

How may this research proceed? First, administrative objectives must be concretely defined. Second, sufficient experimental control must be exercised to isolate the problem are from disturbing factors. These two requirements have rarely been fulfilled in so called “administrative experiments”.

“Perhaps the program outlined here will appear an ambitious or even a quixotic one. There should certainly be no illusions, in undertaking it, as to the length and deviousness of the path. It is hard to see, however, what alternative remains open. …

It may be objected that administration cannot aspire to be a “science”; that by the nature of its subject it cannot be more than an “art”. … [But] even an “art” cannot be founded on proverbs.”

Decision Making and Problem Solving by Herbert A. Simon et al. — A Summary

Topic: Decision Making and Problem Solving
Authors: Herbert A. Simon et al.
Publication: Research Briefings 1986: Report of the Research Briefing Panel
on Decision Making and Problem Solving (1986) 


What lies at the heart of everything that gets done is decision-making and problem-solving. Problem-solving includes fixing agendas, setting goals and designing actions and decision-making is evaluating and choosing the options thrown up by problem-solving actions. Both of these processes should happen effectively to address general and local problems.

In this age, it is not just humans but machines that hold the abilities and skills which make problem-solving and decision-making possible. How humans can use computers for enhancing how they make decisions and solve problems is one fertile avenue for further research and advances. In fact, much research has already been done and findings have been put to good use.

Subjective expected utility (SEU), a sophisticated mathematical model of choice, has informed much of our prescriptive knowledge on decision-making (not problem-solving). It is based on conditions of perfect utility-maximising rationality in a world of certainty. Empirical research, however, demonstrates that problem-solving is a selective and heuristic process given the limits on rationality and information. This is extremely crucial.

The real world of human-decisions is not a world of ideal-gases, frictionless planes, or vacuums. To bring it (decision-making) within the scope of human thinking powers, we must simplify our problem formulations drastically, even leaving out much or most of what is potentially relevant.

The growing relevance of descriptive theories is forcing prescriptive theories (SEU, for example) to amend their methods and assumptions. The elements of “unrealism” are being replaced by what is actually “attainable”. This alteration has strong implications for research in decision-making and problem-solving.

“Outline of current knowledge about decisison making and problem-solving”

Decision Making

SEU Theory

SEU theory assumes a consistent utility function (a subjective ordering of preferences) and knowledge of the consequences of all the choices on that utility function. Based on these assumptions, it then seeks to determine how an actor would behave. This enables the marriage of subjective preferences and objective data.

The assumptions are very strong and they correspondingly lead to strong inferences. Most tools of modern operations research use SEU theory to determine the maximum that can be attained under certain given conditions.

The Limits of Rationality

SEU is extremely limitated when it comes to handling complex problems because complexity introduces uncertainty. It also makes enormous demands on information which is not forthcoming under most real world situations. The result is that study of actual decision-making processes have to substantially depart from the SEU framework.

Limited Rationality in Economic Theory

Predictions of economic behaviour based on the assumptions of perfect rationality and complete information give extremely different answers from those that assume limited rationality and incomplete information. The latter accounts for a bigger range of the behaviours that are seen in the economic arena. As such, while the assumption of profit maximisation is still acknowledged, what has changed is the understanding that profit maximisation is sought within the limits posed by incomplete and uncertain information.

The Theory of Games

SEU theory fails in situations where are conflicts of interests. Game theory is the most ambitious attempt to answers questions that are thrown up by conflicts of interests. The terms of the Prisoner’s Dilemma[1] closely resemble those between organisations (nations, for example).  And just as the game predicts, opposing parties tend to “satisfice” rather than to “optimise”.

Empirical Studies of Choice Under Uncertainty

  • “When people are given information about the probabilities of certain  events, and then are given some additional information as to which of the events has occurred, they tend to ignore the prior probabilities in favour of incomplete or even quite irrelevant information about the individual events.”
  • “When asked to estimate the probability that 60 percent or more of the babies born in a hospital during a given week are male, people ignore information about the total number of births.”
  • “There are instances in which people assess the frequency of a class by the ease with which instances can be brought to mind.”
  • “When asked whether they would choose surgery in a hypothetical medical emergency, many more said they would when the chance of survival was given as 80 percent than when the chance of death was given as 20 percent.”

Methods of Empirical Research

All of these point to a need to improve research methodology. Some useful developments include the insistence on specific rather than general questions while keeping in mind the fact that how the question is phrased will have a significant bearing on the answer. Data obtained from the field is being supplemented by data obtained in the laboratory. Choice behaviour is studied as it happens not when it happens. Putting all these findings and techniques together in an empirically founded theory of decision making is what lies next.

Problem Solving

Contemporary Problem-Solving Theory

Data gained from laboratories settings have been supplemented by field studies of professionals solving real-world problems in developing a problem-solving theory. The problem-solving process that has been understood from empirical studies can be described in the following manner.

Problem-solving involves a selective search through a wide range of possibilities using heuristics (or “rules of thumb”). This search is helped by procedures like “hill climbing” and “means-ends analysis”[2] that allow the problem solver where to look next or what options to adopt as appropriate for the problem at hand. Problem-solving also depends on a large amount of information that the person doing it possesses.

Contemporary problem-solving theory thus accounts for “intuition and judgment” by locating them in the information and the inferential power that the researcher has. If they do not work, the researcher falls back to the processes of analysis.

Expert Systems in Artificial Intelligence

Research in artificial intelligence (AI) has benefited from and contributed to human problem solving. AI programs called expert systems have been built that resemble the typical human expert in terms of the information that they hold. While the computer programs are more analytic, the human experts will be more intuitive. The difference, however, is of quantity and not of kind.

Dealing with Ill-Structured problems

Complex ill-defined problems that have the capacity to be successively transformed in the course of the investigation are called ill-structured problems. An example is the problem facing an architect. Expert systems, in this area, have to not only know the design criteria but also know about them methods that will satisfy those criteria.

Setting the Agenda and Representing a Problem

Setting the agenda is important because resources are limited and not all problems can receive equal and sufficient attention. This first step in the problem-solving process remains poorly understood. The way a problem is represented depends a lot on the quality of solutions to be found. This is even less well understood.

Computation as Problem Solving

The use of computers for problem-solving has so far been substantial the domains of science and engineering.  In fact, computation has become an object of explicit analysis itself along with the science it does. Computing power augmented by AI has successfully been deployed to chew through the incredible mass of data that is being produced by scientific instruments.

“A brief review of current research directions.”

Extensions of Theory

Decision Making Over Time

Tastes and priorities change over time. This makes the time dimension of decision extremely problematic.


The reality of varying societies or organisations makes it impossible to apply insights on problem solving and decision making across the board. How can this problem be resolved?


How does the behaviour of a person in his capacity as an individual differ from his behaviour as a member of an organisation? Also, while organisations tend to display a sophistication far beyond those of individuals, novelty situations lead to rather inappropriate responses.


From understanding how intelligent systems work, attention is now turning to how systems become intelligent. Learning is important for successful adaptation to an environment that is changing rapidly.

Current Research Programs

[This section outlines basic funding patterns as was current during the time of writing which is not germane to the current situation and, importantly, has little serious theoretical value.]

“Some of the principal research opportunities.”

Research Opportunities: Summary

  • Empirical studies
  • Decision making in organizational settings
  • The resolution of conflicts of values (individual and group) and of inconsistencies in belief.
  • Setting agendas and framing problems


[1] “In this game between two players, each has a choice between two actions, one trustful of the other player, the other mistrustful or exploitative. If both players choose the trustful alternative, both receive small rewards. If both choose the exploitative alternative, both are punished. If one chooses the trustful alternative and the other the exploitative alternative, the former is punished much more severely than in the previous case, while the latter receives a substantial reward. If the other player’s choice is fixed but unknown, it is advantageous for a player to choose the exploitative alternative, for this will give him the best outcome in either case. But if both adopt this reasoning, they will both be punished, whereas they could both receive rewards if they agreed upon the trustful choice (and did not welch on the agreement).”

[2] Hill-climbing and means-end problem solving are heuristic problem-solving strategies. In the hill-climbing heuristic, you simply choose the alternative that seem to lead most directly towards your goal state. In means-ends analysis, you divide the problem into a number of sub-problems (or sub-goals), and then you try to reduce the difference between the initial state and the goal state for each of the sub-problems. (For more: