This chapter concludes the book, not the part of the book.

As noted in the introduction, we can construct several compasses, representing change in an institution over time, or for comparing institutions to each other. The construction is sensitive work due to our making qualitative decisions, as indicated in Sect. 5.4.2. But it is not impossible.

  • Remark 1: The first philosophical remark is that our analysis can be shallow or deep.

Prima facie, when we make our analysis, we focus on outliers. An ‘outlier’ statistic is one that influences the length and direction of the final arrow disproportionately to the others. It has a long length, and maybe a degree that is outwith the other indicator arrows in that sector. If the degree is very different, then it is qualitatively independent of the others. To be shallow in our analysis, we simply erase it and replace it with a ‘better behaved’ statistic, or we can change degree and length directly on the table, and hope that no one notices.

If we are manipulating the spheres, we can also change the relative proportions of the three spheres. Of course, with such gerrymandering, objectivity is compromised, since rather than deciding on the direction and length independently of the other indicator arrows, we do so with respect to the other indicator arrows and in particular, with respect to the final arrow and its representation on the compass. This is trickery, but might be enough to justify a policy in the short term or to a gullible audience. The robustness checks counter the gerrymandering, so it is with a less robust compass that gerrymandering is easier. Remember that if we have been careful in gathering data, and have made the data gathering and analysis inclusive of the wider public, then there should be no surprise outlier arrows missing in a robust compass.

A deeper analysis can be made by working out how to change the reality behind the length and direction of particular indicator arrows. This can be done through changes in policy that address that statistic directly. For example, if an alarming number of people are dying of lung cancer, and this is statistically correlated to smoking heavily, then we design policies to encourage general lung health, and decrease smoking.

An even deeper analysis involves looking at the statistics more thoroughly: re-examining the context and culture that give weight to the decisions concerning degree and length. We can also look for the underlying causes of the statistics within the predominant or secondary sector (we look back to the sector arrows), or by looking at the whole, hence the holistic aspect of the analysis. Returning to the smoking example, we notice that smoking is considered to be ‘cool’ in some sub-cultures, so we can try to counter that image in the broader media. We might also notice that it is associated with rebellion or disquiet. In this case, we might want to look for means of making people feel more at ease and in accord with society.

  • Remark 2: The compass design is not ad hoc with respect to the three qualities.

We might be tempted to change the compass by changing the qualities. We can do this in two ways. One is numerical: by adding or subtracting general qualities. The other is nominal: by changing the three general qualities to another three general qualities.

Let us start with the number three. We could add more general qualities. This is counter-recommended for the following reasons. The three qualities are conceptually orthogonal to each other. That is, they were chosen to be in some sense incommensurable. A final arrow can rotate from any of the three qualities into the other two directly: clockwise from harmony to discipline, counter-clockwise from harmony to excitement, from excitement to harmony and from excitement to discipline, from discipline to harmony and from discipline to excitement. Yet every institution displays one of these predominantly. They are not strict opposites, even if they are incommensurable. Yellow is incommensurable with red, but it is not it’s opposite, being a plant is incommensurable with being a mammal, but it is not its opposite.

The representation in two dimensions allows for three incommensurables but not more. If we had two or four then there would be opposites on a two-dimensional representation. We cannot make an institutional compass by dividing the circle into four since this would suggest that the two opposing quarters are opposites when we tie our sector arrows with a rhombus. Thus, to mathematically represent the orthogonality/conceptual incommensurability of four general qualities, we would need a three-dimensional representation. Our circle compass on paper would have to be a sphere. This is by no means mathematically difficult, or difficult to represent with computer interface, since we can show it being rotated around, but such representations are of limited use because they are difficult for us to see and understand, despite the computer interface. Remember that, here we are interested in making policy decisions based on a simple final representation, and the simplicity is what would be sacrificed. Similarly, for five general qualities, we would need four dimensions and so on. For reasons of ease of understanding, reading and communicating the representation on screen or paper, three is better than a larger number of general qualities.

What of two? We could opt, not for a circle, but a scale of bad to good. This is counter-recommended because it is too dichotomous and simplistic. There already exist decision making aides that are dichotomous, and some are quite suitable. The compass shows its worth in subtle and complex decisions, when there will be some compromise and we are looking to satisfy various stakeholders who have agendas that conflict on some points. Our relationship with the environment is a complex matter, and we should not over-simplify it. So, in decisions concerning our relationship to the environment, the compass is recommended over other decision aides. ‘Good’ and ‘bad’ are relative terms and are sensitive to context and time. Forgetting these sensitivities is what leads to poor policy decisions. After all, some discipline is good: some businesses need to fail to teach us lessons and encourage us to increase efficiency, some social strife is good since it makes society more resilient, it tests and sharpens our sense of democracy. Some stress on the environment is good since, in the right measure, it promotes health and resilience.

An important reason to have three unranked qualities is that it removes us from our dichotomous thinking. We return to the fox or trickster character. See Sect. 5.6.2. We can embrace the subtleties and nuances of complex situations because the complexity is resolved into a simple and intuitive representation. This is what has been missing in the past, and might be one of the reasons politicians are reluctant to make too nuanced decisions. They are difficult to communicate and justify. The compass helps with this, not only the communication, but also the appreciation of the complexity and subtlety. These are the several reasons for three qualities, as opposed to another number.

A final reason for the number three has to do with how our brains work. The numbers: one, two and three only require one part of the brain, and even very young children and many mammals can distinguish these numbers. The number four and all higher numbers require more parts of the brain.

The nominal change is to re-name the three general qualities to three others. The very strong temptation for many people will be to think in terms of good, bad and neutral, or overshoot, undershoot and tipping point. This is scalar-quality thinking. We have this implicitly in the compass when we decide on the length of an indicator arrow, we have it implicitly when we draw a wish spot. But we do not want this to be the end of the analysis. Restricting ourselves to scalar-quality thinking is discouraged because it requires only “fast” thinking. For careful decision making, we want “slow” thinking (Kahneman). The three chosen qualities impose a rational remove that slows our thinking just enough, without becoming too complicated. The doughnut representation in Fig. 5.2 and the Fig. 5.7 representation could be done in a linear way. We just cut the circle in the doughnut and present it in a line. With the representation in Fig. 5.7, we stretch out the centre of the circle and line the indicators up next to each other. We can do this because there is no reason for the order of the indicators, in those representations; we do not need to close the circle. The compass, in contrast, demands the circle – because of the triangle we use to tie the qualities/sector arrows together, and because the rotation of a wish spot or some of the indicator arrows can take place both clockwise and anti-clockwise. The qualities merge into each other, as represented by colours, and as degrees are associated with adjectives.

We could, of course, not use scalar qualities, but another three incompatibles. For example, in deciding on the general quality and suitability of a piece of fabric, we might want to compare durability, beauty, insulation. Durability would be indicated by how easy it is to tear or wear out the fabric; beauty by possible patterns imprinted, shininess, pliability; and insulation by togs, noise penetration, wind penetration and so on. A piece of fabric is not an institution, so we have widened the scope in terms of what the compass arrow exercise can be applied to. Moreover, the three qualities are no longer related to each other.

Changing the qualities is possible, but remember where they come from. The three qualities come from Indian and other Oriental philosophical traditions: Hinduism, Jain and Buddhism. The original words in Sanskrit are: sattva for harmony, and tamas for discipline and raja for excitement. A guru will assess and advise someone based on which of the three qualities predominates in that person and which needs more emphasis. While raja is exciting and glamourous, it can easily tip into tamas , as we try to hold on to the excitement and glory. The path of wisdom is to follow a more sattvic life, tending a little towards raja . According to Kumar (2007) from whom I draw inspiration, sattva is also the better direction for living in harmony with the environment, and better for the environment in and of itself, which is one of the reasons I chose to translate sattva as harmony. This was the reason for these particular three qualities, but I hold out an invitation to experiment and try others. This will be important if we are trying to make the compass relevant to people in other cultures where they might have their own trio of general qualities.

  • Remark 3: There is an inescapably normative component to compass construction.

This does not make the construction subjective. On the contrary, the exercise of constructing a compass turns the norm into a description at the point where we declare the norm and make it explicit. Because compass construction is partly formal the norm is acknowledged and then taken out of our hands by being put into the calculation for generating a final compass reading. One norm is weighed against other competing norms. They are all put in the mix together. Even with the generic compass, normativity enters the exercise as we decide upon a baseline for a statistic. The conception of baseline, scale and extremes is important and worth discussing. Ideological normativity is more explicit when we add the concept of sphere. We then explicitly select a category of data to treat as more important. We express our decision mathematically by making a normative normalisation. We can debate and override that decision.

For example, there might well be times when we think that it is worthwhile to compromise the eco-sphere, for example to prevent immediate war, or under political pressure from other countries. Some governments deliberately “sacrifice” certain ecological areas in order to make the standard of living higher in the whole country. In this spirit, we would then ignore that sphere and the data on the corresponding table for the sacrificed region, or more subtly, we could reduce the relative size of the eco-sphere. Recall how Mayumi (2001, 45) puts it: as a society, we decide on a culturally acceptable rate of entropy production. The rate at which we consume fossil fuels is balanced against other considerations. In absolute environmental terms, we decide on a rate of environmental degradation when we use fossil fuels. The beauty of deciding on such a rate, is that we take responsibility for that decision. We are responsible for the compensation or trade-off. We are responsible to our own future, to the next generation and to seven generations hence, if we follow the wisdom of the indigenous people of the Americas.

In terms of the methodology for constructing a compass, for example, rather than insist on taking pre-massive population growth and industrial agriculture as the baseline for the eco-sphere, we could decide that the baseline is after that, say, the year 2000. We decide to return to the rates of entropy production – the using up of non-renewable resources, that we enjoyed in 2000. We can do this in absolute terms for the world, and then decide on mechanisms for distribution for the license to use up non-renewable resources. We could decide to do this for a country or other administrative boundary, or we could do this in more relative terms, such as making it a per capita measure.

Following Friend (2017, 101), if we add “per unit of consumption”, then we have a culturally acceptable measure of ecological efficiency. Both are important for the purposes of ecological economics. We might also allow some institutions more leeway in compromising the environment than others. These are trade-off decisions it is better to make consciously rather than unconsciously. This only adds transparency and depth to the analysis. A deep analysis is used to make a solid justification for a policy.

The normative considerations will also be important as priorities change in a culture. It is clear that more of the public in many countries are more concerned about the environment than they used to be. This is partly because the damage is increasing, and so more people have become aware it. In contrast, in the past, not everyone could see for themselves the effects of our collective burning of fossil fuels. But it is also due to our communication with each other, with education in schools or with becoming more open to listening to the voice of indigenous people. The shift in general awareness is due to many things happening in concert. Norms change. Keeping track of changes should be part of policy, and it should be part of economics, insofar as economics is supposed to be about the management of resources.

  • Remark 4: There are checks for robustness of the sector arrows.

By ‘robustness’ we mean that the sector arrows are quite stable (at a time) – adding more indicators does not alter the degree or length of the sector arrow very significantly, instead they re-enforce the sector reading. This means that there are no undiscovered ‘outlier indicator arrows’: ones that significantly change the direction or length of the sector arrow, and therefore the final arrow. This does not mean stability over time, since the outside context can change, especially if we pass a number of tipping points, such as when ecosystems collapse. Policy decisions take place at a time, given the state of the context at that time. This is the best we can do at that time, except to anticipate future changes. Equivalently, we can be careful to adjust policy as the arrow shifts. This means that rather than staying with one policy decision, we match policy to a changing compass situation, we have a policy programme or policy system, as opposed to fixed policies. See the accountancy compass in Chap. 10.

To ensure robustness, or accuracy of the information, we have two checks. (a) We add indicators until we reach convergence on the length and position of the sector arrows, (b) we consult more people involved with the institution when we make the table. We ask them if there is an indicator that we have overlooked, and consult them for assessment of length and position. It might turn out that there is a strong disagreement. We then make two separate indicators. The idea behind wide consultation is that the sector arrows will be more robust if we consult more people who are affected by the institution than only a few people. Different people notice different things. A further advantage of wide consultation is that through participation in data inclusion and in the exercise of determining quality and length of indicator arrows, participants will understand and better accept policy decisions made on the basis of an institutional compass. The decisions being made in a complex situation are not easy, and will not please everyone. Acceptable compromise is reached through understanding the higher purpose.

Some politicians and decision makers sense that their power is compromised by participation. The decisions are no longer exclusively in their hands. For this reason, they would forgo the inclusive part of the compass construction. This omission is counter-indicated. The sense of threat and of loss of power is an illusion. The decision maker who uses the compass method will still have his, or her, name associated with the decision. After all it is him, or her, who decided to use the compass, and who leads the exercise. If the policy is in keeping with the wishes of the people served by the decision maker, then it will enjoy acceptance and longevity. In other words, the fame of the decision maker will be greater, not lesser. He, or she, will be judged favourably by history. This is why robustness and objectivity in the reading are important.

Another way of thinking about robustness is to consider the question: “Under what circumstances would different compass constructors end up with different final compass readings?” This is a question about subjectivity in construction.

There are three reasons why the same institution might have different compass readings. One is that the compass constructors included different data. The recommendation is to include all of the data that is deemed significant – so, amalgamate the data tables.

The second reason is that the analysis of particular data is different – in length or degree. If it is different in length, then other extremes of maximal and minimal are being used and that what is “normal” is also different. These issues bear discussion, and the discussion is important for consensus and mutual understanding.

The third reason is that the normative-normalisation is done with a different calculation. In this case, we confront different world-views. This is difficult to deal with on a decision-making level, since the differences tend to be philosophically fundamental. However, what we can do is try to negotiate a compromise, or take responsibility for holding a view when an alternative has been presented. After all, one of the views might be a more accurate portrayal of reality. We can only test this in the long run, as our choice in world-view and the decisions made in light of that view are played out. The point is to take responsibility towards ourselves and others for our metaphysical conceptions of reality.

It remains that it is not always possible to ensure robustness. The quality of the data might not be high, there might be few participants, there might be very little numerical data. If we lack robustness, then we make policy decisions that address the shortcoming: improve the quality of the data, improve participation. In this way we address the limitations of the compass exercise.

  • Remark 5: Even when we do not have a robust compass, and enough data, we still learn valuable lessons from the exercise of constructing the compass.

It is not always straightforward or possible to construct a compass that meets the robustness checks. In these cases, we develop policy recommendations to get better statistics . We can choose them, and name them very carefully in advance. It is with the material we have, and under constraints, that we do the best we can. We then have an explanation as to why we made a particular policy decision, and a partial justification for the policies, but the explanation includes discussing the quantity and quality of the indicators, so it is a more abstract and technical explanation and justification.

More important, we make our policy recommendations with a degree of humility. By trying to make an institutional compass we have a good sense of why we are hesitating, and we can share this information with those concerned. They can then be vigilant about the success of the policy and understand the importance of having a good suite of reliable statistics. Moreover, we have a pretty good idea as to how to change the policy if we later add new statistics that influence the position or length of the non-robust arrow. So, we can anticipate. After all, when we make policy decisions, we know in advance that we do so with imperfect information, that the context changes, that values change, but we make the decisions despite this. With the use of an institutional compass, even a non-robust one, we do better than just take a guess, trusting our instinct, making the decision on the basis of a monetary calculation to maximise profit or by using alternative multi-criteria decision aides. Again, the latter are entirely appropriate in the right setting and in making certain sorts of decision. The compass shows its worth in truly complex situations.

At the end of the day, compass construction, like modelling, is an art. This is so for two reasons. One is that there is an element of normativity that is inescapable. This is to be expected, since we are normative creatures. We notice only what we can notice, and what we can notice, depends on our education, language, experiences, open-mindedness, desires and the varying capacities of our very senses and instruments. Even when we make a compass exercise widely democratic, we collectively cannot notice every detail. We are not in the position of Laplace’s Demon. What we can do, is make the evaluation as objective as possible, and this includes not omitting or trying to hide our inevitable normative leanings.

The other reason compass construction is an art is that there is a feed-back loop that changes the outcome. In creating a compass, as scientists, we learn more about the institution we study. If we advise on policy, then that policy will, hopefully, change the institution, and this change will be reflected in the final arrow of the compass. This is why it is interesting to try to keep the information as up to date as possible. At the same time as we construct a compass, the final arrow changes. We use the term “in real time”. Of course, the term is disingenuous here. The best we can do is have some of the data be updated, as and when, instruments track a change and it is only then that the arrow can be adjusted. After all, the method of aggregation is an algorithm. It can be written as a computer program. What cannot be left to a computer is cultural changes in quality-attitudes towards facts and data. Some such changes can be noticed indirectly by looking at people’s behaviour, but not all. This is the stuff of consumer profiling. We can use this sort of information for the compass too. But, again, not all of the data analysis can be left to computer programs. The compass engenders a feedback loop when it is used for communication, since it is a representation of where our institution is heading. It acts as a mirror. The mirror is informative, and we react to that information. We react to the representation, to then make or influence policy, and that will over time change the final compass reading, to which we react a new. This feedback-communication loop is an example of data that a computer program cannot handle well.

To extend the compass to work as a non-linear accounting method, we draw an oval in the area to which we hold ourselves accountable . As policy makers, we are then held accountable insofar as the final reading deviates from the oval. There might be perfectly good external reasons for the discrepancy, after all, even the climate is changing, which means that ‘baselines’ also change, pathological behaviours in climate or society become ‘normalised’. Here, we should be aware of the distinction between a statistical norm or mean, and a norm of health or well-being (of an eco-system or of society). The first is a reading of where we happen to be and reflects what we collectively experience as “normal”. The second is scientific. The scientific norms are removed from our experience of them. They are measured. The two norms might not coincide. In fact, increasingly they diverge since we replace awareness of our surroundings with an awareness of a virtual “reality” that is presented to us through a screen. Open discussion and increased awareness of the divergence between reality and virtual reality is necessary for finding a way towards sustainable living.

The management of scarce resources is what economics is supposed to be about. Determining that natural resources are scarce, and managing them has revealed itself to be calamitous when left to organisations or businesses that use neoclassical economics to determine policy. The institutional compass can replace neoclassical economic calculations, but it is not perfect. It is imperfect because we cannot include the trees, the birds or the rivers directly as stakeholders. In lieu, we can ask scientists, farmers and indigenous people for information about the natural environment. They are the best experts we have for evaluating and managing scarce and fragile natural resources.