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Before a policy comes into existence there will have been an awareness that there is a need to be addressed and whose identification and quantification provides the basis for policy conceptualization. Needs are identified as gaps in current provisions and expressed in the form of desirable outcomes. Clearly once a gap, or need, is accurately identified, only then can a means or policy be sensibly designed by selecting the appropriate instruments that can help move the economy from the current to the desired state.



In a participatory democracy there is a distinction made between identified technical and economic gaps, identified by extension officers or economists and needs. Needs are the conversion of identified gaps into a prioritized list, established by the constituents or farmers, as representing their agreed desires that require policy action. Therefore the necessity of establishing sector needs across a wide range of structures, economic and bioclimatic conditions is complex and it can only be achieved through an effective involvement of farmers and their representatives in the whole policy management cycle.

The process of transforming information on needs into practical policies to address those needs is known as decision analysis. The term "decision analysis" was coined by Ronald Howard2 and appeared in the 1960s in his work with collaborators at the Stanford Research Institute, Menlo Park, California, USA. Decision analysis is a disciplined method to decide how to solve complex issues.



Within the domain of decision analysis, a decision is defined as an irrevocable allocation and use of resources to actions designed to implement the processes required to achieve a specific objective. This also means that if such a decision is later altered further additional resources will need to be allocated. As a result the analysis used to arrive at a decision is important.



In 1968 Matheson & Howard noted the evolution of the new discipline of decision analysis which, in their words, seeks to apply logical, mathematical, and scientific procedures to the decision problems of top management that are characterized by the following:
  • Uniqueness - each is one of a kind, perhaps similar to - but never identical with - previous situations
  • Importance - a significant portion of the organization's resources is in question
  • Uncertainty - many of the key factors that must be taken into account are imperfectly known
  • Long run implications - the enterprise will be forced to live with the results of the situation for many years, perhaps even beyond the lifetimes of the individuals involved
  • Complex preferences - the task of incorporating the decision-maker's preferences about time and risk assumes great importance
Decision analysis provides a logical framework for balancing these considerations. It permits mathematical modelling of the decision, computational implementation of the model, and quantitative evaluation of the various courses of action. This combination of considerations enables the production of useful aids to decisions by top management involving the use of corporate resources. Thus although deployed in the resolution of problems within large companies it is also ideal for resolving decision issues surrounding the allocation of resources in local, regional, economic sectors such as agriculture and the macroeconomy.

The decision analysis is a discipline that needs to be applied to all stages in the policy management cycle from needs identification, to decision analysis model building, collecting information, identifying policy options, selecting the most effective policies, implementing the policy, monitoring progress and carrying out subsequent diagnostics on the outcomes.



The essential method of decision analysis is the use of a decision analysis model and this is intended to represent the decision environment showing the relationships between the critical determinants of decision outcomes. The construction of the model involves a design process known as the decision analysis cycle. This has the purpose of refining a decision analysis model on the basis of a proactive evolution achieved through reiteration and testing of the quality of three specific types of information :
  • The information and knowledge used to construct a model which encapsulates the understanding of the cause and effect relationships between the critical factors which determine decision outcomes (deterministic), e.g. the quantitative relationship between fertilizer input and crop yields.

  • The confidence in the applicability of the model rests upon the understanding or estimation of the probabilities of critical events, not all of them under the control of the decision-maker which can affect decision outcomes (probabilistic), e.g. the variation in bio-climatic variables (temperature and water availability) from year to year

  • The utility of the model finally depends upon the availability and quality of the information (data), knowledge and understanding of determinants as well as basis for estimating the likelihoods of decision outcomes (informational), e.g. the availability of accurate information on the performance of a specific production systems across the existing farms and research results relating to determinant factors


Source: SEEL 2000, VisualModel.com 2009 (adapted from Matheson J. E. & Howard R. A.,3)



Before committing substantial public or private resources to actions to achieve specific objectives it makes a lot of sense to determine the best options from the standpoint of feasibility, costs and risks through simulations, based on decision analysis models. By avoiding significant expenditures and investing in analysis beforehand, £ millions if not £ billions can be saved by avoiding loss resulting from costly mistakes and "over-runs".

A decision analysis model is deemed to represent an adequate standard when sufficient confidence has been obtained that the deterministic, probabilistic and informational dimensions cannot be refined further and that an acceptable level of uncertainty has been attained. At this point the decision analysis cycle, concerned with the devlopment of the model, is considered to have run its course. The decision analysis model itself can then be used to conduct qunatirtative analysis, for example to simulate options for decisions according to various criteria and conditions and making use of well-established operations research methods for optimization, cost minimization and sensitivity analysis.

Once a decision has been made to commit resources as indicated by the word "Act" in the red disc in the diagram above, the follow up, based on the collection of information on the outcome, often referred to as monitoring and evaluaton, provides important information to determine the accuracy and effectiveness of the model in predicting outcomes and as a basis for idenifying which information and knowledge was deficient at the time of the last decision. Thus the model not only provides guidance on the decision analysis it can also be used to assess outcomes as well identifying where improvements can help refine future decisions.

Transparency

An essential benefit of decision analysis and the decision analysis cycle is its contribution to transparency. Transparency helps all involved to remain informed about the latest information on the status of application of state-of-the-art technology, techniques and the range of observed performance. The utility of such transparency is maximised when this information is shared with the farming constituency.

Decision-maker preferences

Decision-maker time and risk preferences are fundamental considerations in the establishment of criteria for accepting a decision option providing a basis to proceed in a specific way to achieve an overall objective. Decision-maker time and risk preferences are the expression of the specific timing of outcomes of a decision desired by the decision-maker associated with a specific level of risk. Time and risk preferences are constrained by the given knowledge and information concerning what are considered to be likely time frames within which events might occur or need to occur as well as the likelihood of events. Typically, these two measures arise from the observation of events, for example arising from the analysis of survey statistics or historic evidence. On the other hand the decision-maker, being aware of the “given” or “assumed” time frames and likelihoods of events (outcomes) will be aware of risks to their circumstances or interests associated with such event likelihoods.

If one accepts the evolution of a natural course of events without attempting to alter relationships through decision-making geared towards the identification of means of mitigating risks then such a situation can be characterized as a "fatalistic passivism". Under such circumstance events might well occur according to the predicted statistical probability and thereby create exposure to the attendant risks. It is however, possible to apply decision analysis to review options available to mitigate the likely outcomes by altering the probability of such events occurring. This can be achieved by avoiding exposure to critical events, by changing the methods of achieving desired outcomes which are less dependent upon the risk factors.

In addition to the time preference for the outcome of a decision to be realized there is also a time preference on what would be considered to be tolerable time frames within which results of decisions should become effective (implemented). Risk preferences is the level of uncertainty considered to be acceptable before a decision might be taken and below which no extra resources (costs) need to be used to obtain better quality information at the time of the decision.

Selective preferences

Selective preferences are closely related to the objectives or intent of the decision-maker and can be made up of a range of conditions which the decision-maker wishes to satisfy. These can range from specific objectives such as the maximization of profits, minimization of costs, to a limited range of options deemed to be "acceptable" such as making use of labour resources in specific geographic locations or to select options which can be implemented through means in line with a specific philosophy of market economics.

Selective preferences introduce a significant degree of apriorism into decision-making and this can be formalized in the form of terms of reference for a decision analysis assignment. Terms of reference are often used to limit the scope of decision analysis to options which have been predetermined to be acceptable. This is a common state of affairs in the political domain where a particular philosophy is being promoted by a political party or there is a need to satisfy specific segments of the constituency. It therefore needs to be appreciated that the decision-maker's selective preferences impose certain constraints upon decision analysis model's output and thereby reducing the number of potentially feasible options from which to select. This can mean, where only those options satisfying decision-maker preferences are presented, that the full range of feasible options will not be exposed and the decision maker can remain unaware of options which might have been preferred.

Resource constraints

In order to implement a decision there is a need to specify the quantities and qualities of implementation resources in the form of manpower, equipment, energy, variable inputs, space, information and finance to purchase or lease critical resources and inputs. A decision-maker sometimes needs to identify and quantify the resources previously committed as a result of former decisions on the creation of a “budget line”, or able to be committed, to the implementation of decisions and thereby establish a physical capacity limitation and/or budget. Once these are defined there is an imposition of a constraint on the scope, scale and therefore range of feasible decision analysis model options that can be generated within the pre-established envelope of resources available.

Exogenous factors

Exogenous factors are those events which are known from past experience to be capable of influencing decision outcomes but over which the decision-maker has no control. Typical examples include drought, flooding, earthquakes and other "acts of God". On the other hand other factors such as early and later seasons which can affect agricultural production to a significant degree and impact decision outcomes. Market shifts affected by such matters as international tensions, droughts and crop failure can have significant impacts on financial assets backed by futures contracts on commodities and therefore affect decision outcomes. There is a spectrum of such effects from those classified as probable to those which are largely indeterminate but which could occur but with no confident estimate of probability.

What has this got to do with the Real Incomes Approach to economics?

In the article Micro-macro coherence it is explained how it is possible to relate objectives at the macroeconomic level which can gain a unanimity of support at the macro level. This can be presented in the form of an Organizational Elements Method table shown below:

The Real Incomes Approach
The Organizational Elements of Price Performance Policy (based on Kaufman's OEM)

Name of the Element & Level of planning & action
Brief Description
1
Macro-SystemThe state of real incomes distribution in the context everyone in the social and economic constituency.
2
Outcomes/Macro Higher better distributed real incomes, higher employment, lower inflation, higher currency purchasing power, higher exports, import substitution, higher productivity
3
Outputs/Micro Lower price performance ratios, competitive prices, accessibility to products and services
4
Products/Sub-MicroHigher productivity, accumulated tacit knowledge, refined technique, better use of explicit knowledge
5
ProcessesThe ways, means, activities, procedures and methods used internally
6
Inputs The human, physical, and financial resources used in the processes


Although Kaufman4 uses the term process only at the microeconomic level 5 the reality is that the whole table is a process that can be modelled on the basis of a decision analysis model. This is better visualized by rotating the table clockwise through 90o as shown below. The representation is of three companies A, B, and C each supplying the macroeconomy. Each sequence of inputs-processes-products-output within each company is a process that can be modelled using a decision analysis model and using input-output analysis using standard operations research procedures to simulate operational options designed to maximize real incomes of owners and the workforce, while lowering unit output prices, thereby augmenting purchasing power of the currency and real incomes of consumers. Since the macroeconomic objective is also the maximization of real incomes there is a coherence between micro and macroeconomic objectives. The Real Incomes Approach in the form of Price Performance Policy (PPP) provides the direct incentive for companies to operate in this manner though the deployment of transparent business rules.(See, Appropriate business rules for competitivity & growth)

The Real Incomes approach
The Organizational Elements transformed into a series of decision analysis models

              Microeconomies
Macroeconomy

Higher better distributed real incomes,
higher employment, lower inflation,
higher currency purchasing power, higher exports,
import substitution, higher productivity

AInputsProcessProductsOutput

BInputsProcessProductsOutput

CInputsProcessProductsOutput


An essential quality of this decision analysis approach to the Real Incomes Approach is that each company is completely free to optimism their operations according to their specific conditions, capabilities, access to and their ability to use resources in a policy environment that provides transparent incentives to maximize real incomes through effective and efficient resources allocation.

Decision analysis & public choice

An important potential role of decision analysis is in providing information that supports public choice, an essential component of constitutional economics. However, it is important to manage the decision analysis in such a manner are to minimize selective preferences and to expose a wider range of options. This signifies that the normal terms of reference for "studies" and "reviews" should not be biased by government (read political party) preferences since this can result in the full range of feasible options not being exposed and the public, therefore, remaining unaware of options which might have been considered to be preferable.

In terms of the elusive need for unanimity in public choice the Real Incomes Approach has a pragmatic approach. By orientating the economy towards a single transparent objective, that of sustaining and increasing real incomes, the feasibility of achieving Kaufman's "Mother's rule" which has been shown to represent universal macro values such as freedom from poverty and disease, decent educational opportunities, satisfying work options, the protection of the vulnerable and the equitable distribution of income and the avoidance of aggression and warfare in foreign relations, is enhanced. The satisfaction of motherhood values cease to be a collateral and indirect impact of economic policies but become an integral part of normal economic activities.

DABs-Decision Analysis Briefs

In order to bring some coherence and order to policy formulation I proposed Decision Analysis Briefs (DABs) as an essential constitutional requirement5 to accompany any policy proposition in order to demonstrate that the proposition represented the most economic, effective and efficient means of achieving the stated objective/s. The current levels of assertion and partisan bias as well as fuzzy content of the manifestos of the main British political parties, especially in the field of economics, is deplorable. There is also a wanton arrogance in the practice of some politicians making pre-election undertakings not to impose specific actions only to impose them on gaining power or, indeed, the inverse of promising specific actions and then refusing to follow up having won the election. DABs represent a desirable constitutional requirement for objective evidence in support of any policy proposal and would seem to be a rational requirement of any civilized society that supports an open communication strategy in support of an informed citizenry and an imperative for a peaceful and productive participatory democracy.

When decisions are taken it is of fundamental importance that they are considered by all to be rational and fair and under all circumstances we should proceed to avoid discontent and recriminations. A fundamental quality of decision analysis in the context of constitutional economics and the Real Incomes Approach is that it can be designed to have a fundamentally ethical strand that is supported and enforced through appropriate legal frameworks that do not constrain the productivity of the economy. The specific issue of ethical decision analysis is the topic of an article in preparation and which will be posted shortly.


2 Ronald A. Howard has been Professor in the Department of Engineering-Economic Systems (now the Department of Management Science and Engineering) in the School of Engineering of Stanford University since 1965. He earned his Sc.D. in Electrical Engineering from MIT in 1958. He was Associate Professor of Electrical Engineering, Associate Professor of Industrial Management, and Associate Director of the Operations Research Center at MIT. He is one of the founders of the decision analysis discipline. His books on probabilistic modelling, decision analysis, dynamic programming, and Markov processes serve as major references for courses and research in these fields. His experience includes dozens of decision analysis projects that range over virtually all fields of application, from investment planning to research strategy, and from hurricane seeding to nuclear waste isolation. In 1986 he received the Operations Research Society of America's Frank P. Ramsey Medal for Distinguished Contributions in Decision Analysis. In 1998 he received from INFORMS the first award for the Teaching of Operations Research/Management Science Practice.
3 Matheson J. E. & Howard R. A., "An Introduction to Decision Analysis", Stanford Research Institute, 1968
4 Kaufman, Roger, Alicia M. Rojas, Hannah Mayer (1993). Needs Assessment: A User's Guide. Englewood Cliffs, New Jersey: Educational Technology Publications, Inc.
5 McNeill, H.W., "The Briton's Quest for Freedom-Our unfinished journey...", Chapter 26, "Parliament", "Decision analysis brief" pp: 273-287, Chapter 27, "House of Lords", pp:289-296, 418pp, HPC, 2007 ISBN: 978-0-907833-01-7


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The Decision Analysis Initiative 2015-2020
George Boole Foundation