ࡱ>  UbjbjVV K<<(MIS ) 3 8$#45)(W:< V b ((((((($],.<(j H< j j (??y(p#p#p#j ?R((p#j (p#p#:<(,"|(=OL!h( ()05)r( ;/";/|(;/|($j j p#j j j j j ((p#j j j 5)j j j j ;/j j j j j j j j j : CONTROL OVER CHAOS: A SYSTEMS CRITIQUE OF PROGRAM LOGIC Graham Smith Abstract Using a systems theory approach, this paper discusses some problems in the use of program logic. Program logic is a cornerstone of evaluation, but the logic is sometimes overly complex, may not cover all key eventualities, and most significantly, may fail to explain the program results. The logic is in place, but the program still fails. Why is this so? Using systems concepts from complexity theory, chaos theory and complex adaptive systems, the paper points out that the linear deterministic network of a typical logic diagram (formulations such as input to activity to output to outcome to impact) are not rich enough to deal with the program considered as a system. This requires consideration of context, feedback, outside influences and the possibility that the system is adaptive, i.e. the outcome is a goal to aim for, not just a mechanistic consequence that follows from applying the program inputs. However, all is not lost. Systems may be complex but they are often stable. This control over system behaviour is achieved through feedback, an element that conventional program logic finds it hard to deal with. Studying these controls can help to clarify the behaviour of a complex system and understand why unexpected results occur. Introduction You may recall the old TV series 'Get Smart' featuring a bumbling secret agent working for an agency called Control, fighting against a dastardly organisation called Kaos. The contest between chaos and control is a major theme of this paper. Chaos now has two meanings. One is the everyday one of things going awry in a messy and unpredictable manner. We talk of the chaos of war, or even the chaos of family life. It is disorganisation writ large. It comes from the Greek, where it meant the formless chasm that preceded the creation of the universe. By extension, utter confusion and disorder (OED). The second meaning of chaos is a mathematical one. In this case, there is apparent chaos (in the first sense) but it is really deterministic if unpredictable complexity. Chaos describes the behaviour of dynamical systems that are highly sensitive to initial conditions (wikipedia). Variations in initial conditions can lead to unexpected results, leading to such concepts as 'strange attractors'. The outcomes of chaotic systems, however, are deterministic identical input conditions will produce the same results. The point is that input conditions that are microscopically different may lead to radically different outcomes. An example of chaos is the game of snooker. Commencing with an ordered arrangement of balls on the table, the initial stroke then casts the balls around the table. The outcome of each collision in terms of the direction and velocity of the colliding balls is predictable by simple Newtonian mechanics. However, minute variations in the direction and velocity of the initial stroke radically change the final disposition of the balls. The final location of a ball is almost impossible to predict if there are two or more collisions with other balls. Chaos results from order. Control is a word that has even more meanings than chaos. At is heart, it refers to the art or practice of imposing order on to an unruly universe. You are in control of a car, of your life, of anything if you have the ability to react to any bumps and other unexpected events and restore the system to that which you intend for example, the car in the middle of the lane, going at the right speed in the right direction. The every day meaning of control includes to constrain (e.g. to control your emotions) and to exercise authority. (OED) It also has the meaning of to verify (check the accounts hence the title of Controller) and it is still used in audit with the specialist meaning of a mechanism that can act to prevent or deter an undesirable outcome. (e.g. the risk is that the holder of the firm's cheque book will commit fraud by misusing cheques the control is to require two signatures on each cheque). Deterministic chaos and the program logic model A key instrument of thinking in evaluation is the program logic model. In its simplest manifestations, it consists of a small number of steps, e.g. input activities output outcome impact This is a useful discipline in order to establish minimum criteria for a program, i.e. that there is actually a feasible connection between what the program does and some desirable social consequence. Some logic diagrams are quite complex and involve a large number of interactions. For example, here is a logic model established for a crop improvement rural development program. (Duignan 2011)  There is a risk that this string of connections is similar to the sequence of cannons required in a snooker shot. Achieving the impact is a potential logical consequence of providing the input, but if the snooker analogy holds, then each link in the chain must be very reliable or the chance of achieving the planned impact is low. In addition to the problem that the required objective might not be met, there is the possibility that there may be adverse consequences. Perhaps this failure of 'all the balls to connect in the right way' is one way of thinking about unexpected program failure. For example, the home insulation program in Australia required that several processes, from marketing of the program, recruitment of insulation firms, establishing minimum standards, selection of insulation provider by the occupant and the installation of insulation all worked correctly. Not all correcting feedback loops, e.g. to check the bona fides of installers and to set and monitor standards, were operating correctly. (Hawke 2010; ANAO 2010) Complexity theory and complex program logics Complexity theory is one way of addressing that class of problems that are not amenable to reductionism because there are too many interactions to consider. Such systems approaches consider emergent properties, properties that are a consequence of the system as a whole rather than the specific components of it. Chaos theory is sometimes considered to be the subset of complexity theory that deals with deterministic systems those without purposeful feedback loops. The other major component is complex adaptive systems (CAS) One of the key issues in complexity is feedback loops. With respect to program logic this has (at least) two issues: One is that the concept of 'level' in the program logic (e.g. outcome level, intermediate level etc.) becomes of reduced value. In principle, everything can connect to everything through feedback loops. In particular, the results achieved to date can feed back to activities and inputs. Secondly, there is the feedback that the model can have on reality. The model may only be an interpretation of reality, but its very existence can shape perceptions among participants and owners, and such perceptions can then influence the actuality. With such difficulties, can there be a useful program logic approach to complex issues? I have my doubts. There may be contending approaches or philosophies, but they are hardly program logics. To take the common exemplar of a complex task of raising a child (complicated = sending a man to the moon; complex = raising a child) there are various philosophies or traditions that can be expressed as program logics if you try hard enough. Two overly simplified logic trains follow: IF I observe my child's behaviour carefully AND detect rigorously when behaviour does not meet norms AND provide appropriate punishment THEN the child will have incentives not to repeat such behaviour AND THEREFORE will become better behaved LEADING TO better conformity with social norms LEADING TO improved chances of success as an adult. IF I provide my child with unconditional love and support THEN they will be more confident AND will be motivated to return that love and respect AND seek to act so as to gain approval by following the norms demonstrated by their parent THEREBY adopting the norms of society while also having the confidence to make appropriate explorations LEADING TO improved chances of success as an adult. Although these 'program logics' each have face validity, they are quite different and at least partially contradictory. They are based more on personal preference or prejudice than on any 'logic'. This is obviously the case for child-rearing, but I would contend that other complex issues of more public concern also have two or more schools of thought contending, each with their own 'program logics' that are internally consistent. Health insurance, crime reduction and asylum seeker policies come to mind. Resolving such issues is probably better done through a political or dialectic process. The dialectic approach considers two contending theses and then aims for a synthesis that contains both. To continue with the oversimplified example before: Thesis: Strict rules and punishment is the best way to bring up a child. Antithesis: Love and trust is the way to bring up a child. Synthesis: Love together with the setting and clear communication of behavioural boundaries is the best way to bring up a child. This is a long way from program logic. The question is whether program logic is adequate to describe complex issues. The role of control With control, we come to some systems related issues. As in the example of controlling a car discussed above, control can mean to provide feedback to system parameters such that the desired level is maintained. This is very explicitly laid out in the concept of 'control charts'. An example, taken from the Wikipedia definition of control charts, follows: Figure 1. Example of a control chart In this case, a characteristic of interest is plotted as it varies with time. The target value and upper and lower control limits (UCL and LCL) are established. In the example above, observations are in the acceptable range to start with, but then diverge unacceptably. In electronic circuits, negative feedback loops provide for stability of performance. The main characteristic, for example the extent of amplification, is set by the proportion of current output that is fed back to the input stage. Positive feedback leads to lack of control, e.g. when sound from loudspeakers is fed back to the microphone, leading to the unpleasant noise called feedback. In a personnel assessment system, the term feedback is also used. If the employee is doing something undesirable, they are provided with negative feedback: information and incentives designed to stop or reduce the undesirable behaviour. If they are succeeding, they are provided with positive feedback. In the electronic analogy, this is unstable because the volume increases in milliseconds to an unpleasant level. This hazard does not apply in personnel assessment, as the feedback loop takes much longer, and if performance continues to improve, there is no such thing as an 'unpleasant level'. Many systems have natural feedback and control systems often so natural that we do not notice them. Perhaps the exemplar is ecological systems. There can sometimes be excursions of population densities, but in a well-functioning ecological systems of predators, prey and resources, the population will tend to return to a relatively stable level. The way of reaching the outcome is the opposite to the billiard ball example. It sounds heartless, but it almost does not matter if an individual creature lives or dies the population in a generation or two is not affected at all. The future of the population depends not on what happens to individuals but on the nature of the ecological resources, in particular habitat. Returning to the use of logic models in evaluation, what do we find? Most discussions of logic models recognise that in addition to the links from inputs to processes to outputs to outcomes, there is the need to consider external influences, feedback and context. However, there can be a tendency to considered these factors as 'noise' to be managed and minimised. A depiction of this process is as follows (where the width of the lines indicates the importance given to the aspect). Figure 2 Logic Model description of a program  This diagram shows an emphasis on the interactions between inputs, processes and outputs to achieve the final outcome, with context, constraints and feedback noted, but considered secondary. The diagram below indicating the systems approach contains the same elements, but with different emphases. Figure 3 Systems description of a program  In the systems approach, although the details of the interactions are recognised, there is a sense in which they are regarded as a 'black box'; and within that black box, feedback loops are as important as the direct impacts. The more important elements are to consider how the inputs, the context and the constraints affect the overall system. And although not formally depicted in this diagram, in systems thinking we can consider the extent to which the outcome is not 'dumb'; it may be an active goal to which the system is directed. An interesting example of the importance of considering incentives outside the system is the work of John Ionnidis in debunking medical research. (Friedman 2010). It is important not only to consider the internal processes of scientific method and peer review, but also the external incentives in the form of publication credits and pharmaceutical company incentives in assessing how medical research gets conducted and reported. An example of Control versus Chaos: Free Market and Central Planning economies One of the features of communist regimes was the use of central planning to direct their economies. Knowing that x million hectares of farming required y thousand tractors, the Soviet planners would make this happen, through planning the manufacture of tyres, components, machine tools, and provision of rubber, steel, petroleum and human resources. In other words, they would calculate the required quantities all the way back through the supply chain and plan for their production. Surely this is more efficient and less wasteful than the capitalist approach, where no-one knew for sure what the demand might be, and various entrepreneurs went about seeking to meet that uncertain demand; some would fail and there would inevitably be waste and duplication. Well, no. The reason for this is that the capitalist system has many more self-correcting features. Correctly estimating market demand and making things efficiently can generate major wealth, the converse penury. Signals throughout the market, primarily price, but also communication and marketing, enable consumers and producers to, in effect, cooperate productively. Innovation and efficiency are rewarded and waste punished. To go back to the diagram: central planning emphasises the how all the components interact in a detailed manner. Capitalism cares little about the details e.g. which firm lives or dies but sets boundary conditions such as legal constraints for example to collusion, exploitation and pollution, sets additional overall incentives and penalties such as grants and taxes, and then lets the market sort it out. Synthesis the best of both logic models and systems thinking The critique above does not recognise the more sophisticated approaches to logic model development. For example, many evaluators distinguish between theories of change and theories of action. Funnell and Rogers (2011) specifically incorporate within the theory of change the requirement to conduct a situation analysis that explores the wider context of the program. This is similar to the systems approach of exploring environmental issues such as ownership, customers and social and economic drivers of the system as a whole. Conversely, Noga and Hargreaves (2009) include system elements such as defining boundaries, relationships and audiences, and focusing on overall impacts rather than individual impacts, within the theory of action. Either way, these more comprehensive approaches do take into account an element of systems thinking. However, these approaches are not always implemented, and this paper contends that the systems approach can be used to good effect not only in understanding context but also in helping to understand mechanisms of change and hence improving both the design and evaluation of programs. For example, let us consider the home insulation scheme mentioned briefly above. The theory of action (or theory of change!) seems straightforward in that funding for home insulation can lead to positive environmental and to economic outcomes. Considering the wider systems within which the home insulation scheme operated we can see such features as: A relatively unskilled industry with few barriers to entry A relatively small industry that would need to expand A declining economy leading to a significant level of interest in stressed companies or individuals wishing to take advantage of the available funds Preferring speed of implementation to the institution of controls. Allowing supplier-driven activity as well as demand by home occupiers. Consideration of these systems factors shows that there were incentives and opportunities for taking advantage of the program leading to undesirable outcomes. Even if the risk was confirmed as acceptable, considering these factors could have led to a design that would have recognised the high risk nature of the program, and put in intensive monitoring of usage and outcomes from an early stage. This could have led, for example, to putting in place additional controls on suppliers and on the transactions. As it was, there was considerable chaos. Conclusion This article is a brief and over-simplified discussion of some complex issues. In this over-simplification, the power of the program logic model has not been fully described. Nevertheless, the author has observed several program logic diagrams that have appeared artificial and not representative of context, feedback loops or of potential adverse consequences. A systems approach of considering to a greater extent the overall incentives and constraints, rather than the internal mechanisms by which the program worked, can add a complementary perspective to that of program logic. References Australian National Audit Office. Audit Report No.12 201011:Home Insulation Program, 15 October 2010.  HYPERLINK "http://www.anao.gov.au/uploads/documents/2010-11_Audit_Report_No_12.pdf"http://www.anao.gov.au/uploads/documents/2010-11_Audit_Report_No_12.pdf Duignan, P.  HYPERLINK "http://www.outcomesmodels.org/models/cropimprovement25-attachments/cropimprovement25.pdf"http://www.outcomesmodels.org/models/cropimprovement25-attachments/cropimprovement25.pdf Friedman, David H. Lies, Damned Lies and Medical Science. Atlantic Monthly, November 2010. Funnell, Sue C. and Patricia J. Rogers, Purposeful Program Theory. Jossey-Bass, San Francisco, 2011. Hawke, Allen. Review of the Administration of the Home Insulation Program. 6 April 2010.  HYPERLINK "http://www.climatechange.gov.au/~/media/publications/energy-efficiency/Home-Insulation-Hawke-Report.ashx"http://www.climatechange.gov.au/~/media/publications/energy-efficiency/Home-Insulation-Hawke-Report.ashx Noga, Jan and Meg Hargreaves. Logic Models and Systems Models: Two Sides to the Same Coin? Presentation to American Evaluation Association conference, 2009.  Numerical Advantage Pty. Ltd. 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