Environmental Monitoring

Environmental Monitoring

Decision making

Decision making theory and practice has long been an interest to business and economic analysis. It is important to recognise that a good decision does not always result in a good outcome! That is why monitoring and feedback is integral to good decision making; you need to know if you have achieved the outcome you expected and, if not, why not. A good decision, ie the best available but based on incomplete knowledge and/or wrong premises, which results in a bad outcome is not corrected by making a bad decision, only by improving knowledge. You need to see below the surface of things if you are going to improve your decision-making!

A bad decision may result in you making another one; as Harry Truman allegedly said, "Whenever I make a bum decision, I go out and make another one",

In The Histories , written in 450 B.C., Herodotus makes the following statement:
"If an important decision is to be made [the Persians] discuss the question when they are drunk and the following day the master of the house...submits their decision for reconsideration when they are sober. If they still approve it, it is adopted; if not, it is abandoned. Conversely, any decision they make when they are sober is reconsidered afterwards when they are drunk".

What a strange way to make decisions, you might say (although it might be an early version of left- and right- brain thinking?). There are, however, even stranger methods of making decisions. Russo and Schoemaker's top-10 mistakes in decision-making are:

  1. Neglecting to critically examine your own decision-making process. Most managers like to have their golf swing checked regularly by a pro, but not their decision-making behaviour.
  2. Not keeping data on the results of your past decisions. In golf terms: not recording your handicap.
  3. Interpreting results afterwards so as to protect your own position or that of your boss. Many incorrect acquisitions only prove to be wrong after the buyer has been promoted to the Board of Directors. At that point no one cares to have a good memory any more.
  4. Assuming that a group of highly intelligent people will together automatically make good decisions. Very intelligent people can often make good decisions on a solo basis in their own field, but in a group they're usually a disaster. Being a bit stupid sometimes works wonders in management teams.
  5. Thinking that you've got all the information in your head and then making an impulsive decision. Especially in combination with 6:
  6. Putting misplaced confidence in rules of thumb, by basing your thinking on easily available information and on facts which suit you.
  7. Neglecting to compile facts because you think you know it all anyway. Besides this, factual research is sometimes avoided because people don't know how to draw conclusions from facts.
  8. A one-sided formulation of the problem. Many managers prefer to have just one problem with one solution at a time.
  9. Solving the wrong problem. Many managers like to work on solvable problems and not on insoluble ones. That's why they often look so surprisingly fresh despite long hours, stress and jet-lags. Soluble problems, by definition, are not the problems which you really need to worry about. Focusing all your attention on these makes the apparently insoluble problems genuinely insoluble.
  10. Starting to collect information and draw conclusions without first taking the time to think about the crux of the question or the way the decision should be made. People don't think before they start, fail to take a step back to jump further and don't take the necessary time because they think they need to hurry.

Neil Postman suggested that we focus on eliminating stupidity as a priority; "intelligence" is too complicated and difficult to 'improve' so eliminating stupidity is usually more effective and more efficient!

Decision theory has been the subject of many studies in psychology as well as the theoretic/modelling approaches to develop tools to inform decision-making. One of the major students of decision theory has been Herbert A. Simon, perhaps best known for the concepts of "bounded rationality" and "satisficing"; he notes:

"Prescriptive theories of choice are complemented by empirical research that shows how people actually make decisions, and research on the processes people use to solve problems... This research demonstrates that people solve problems by selective, heuristic search through large problem spaces and large databases, using means-ends analysis as a principal technique for guiding the search".

What chiefly distinguishes the empirical research on decision-making and problem solving from the prescriptive approaches is the attention that the former gives to the limits on human rationality. These limits are imposed by:

  • the complexity of the world in which we live;
  • the incompleteness and inadequacy of human knowledge;
  • the inconsistencies of individual preference and belief;
  • the conflicts of value among people and groups of people; and
  • the inadequacy of the computations we can carry out, even with the aid of the most powerful computers.

The real world of human decisions is not a world of ideal gases, frictionless planes, or vacuums. To bring it 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. There is a considerable body of work known as "heuristics and biases" which focuses on how human beings "fail" to analyse situations in a completely "rational" way.

More recent work by Todd and Gigerenzer, based on accepting the evolutionary benefit of human thinking, looks to see how human analysis succeeds at all in the face of such potential complexity.

Rationality comes in many forms. The first split in Figure 2 separates models that assume the human mind has essentially unlimited demonic or supernatural reasoning power from those that assume we operate with only bounded rationality. There are two species of demons: those that exhibit unbounded rationality, and those that optimize under constraints. There are also two main forms of bounded rationality: satisficing heuristics for searching through a sequence of available alternatives, and fast and frugal heuristics that use little information and computation to make a variety of kinds of decisions.

Decision trees

One traditional tool for decision-making is that of decision trees with its links to Bayesian statistics. The decision tree is a structured approach whereby one explores options and probabilities of outcomes to derive a ranking of solutions. The site linked above looks at business decisions while Peterman and Peters (Chapter 8, Statistical Methods for Adaptive Management Studies) look at forest management examples.

The issues of linking decision-making to monitoring are discussed in Making monitoring work for managers by Lee and Bradshaw. They note:

"Monitoring has become a dominant theme among environmental scientists, land management, and policy makers alike. The number of publications and plans which propose to do much the same, namely detect and identify system state and change, continue to multiply, each suggesting alternative approaches and solutions. Despite considerable effort by various institutions and individuals, effective environmental monitoring remains an unanswered challenge. This is particularly the case for large-scale, agency-led projects ...

"In the following report, we begin a dialogue about an appropriate conceptual framework for organizing and developing a monitoring plan for broad-scale ecosystem management efforts. ... Our general impression is that the monitoring plans that are currently being developed for broad-scale ecosystem management efforts, while they may be statistically sound, often lack an integrated strategy that allows one to easily see why certain information is important and how such information might influence future decisions and investments"

Bosch et al . in Monitoring as an integral part of management and policy making note

"In the business world monitoring is accepted as an integral component of decision-making in a complex and uncertain world. However, this is not always true for the management of our natural resources, despite their importance to humanity. Many environmental monitoring programmes fail to become an integral part of management because they are not designed to help decision-makers."

They argue, "that if monitoring is to contribute to the adoption of more sustainable resource management practices, it must be seen as an ongoing process within the context of adaptive management. Adaptive management approaches, such as that described here, enable the use of both local and scientific knowledge, and the adoption of a continuous knowledge enhancement process. Providing greater understanding of the system helps the community adapt to change, and can also help to determine what components are most affected by change, in order to target research priorities better. At the same time, participation in the processes of monitoring and adaptive management allows individual land managers to acquire greater technical expertise, building on both collective local knowledge and an associated scientific awareness of their particular physical environment. By achieving specific objectives for the improvement of their resource position through a collective effort, land managers develop greater confidence, and that, in turn, ensures the successful continuation of the whole process."

Adaptive management

There are those that suggest that "adaptive management" is a tautology and while this should be the case the common use of the term in the context of environment management has a different meaning. Johnson (1999) has argued, "the overall goal of adaptive management is not to maintain an optimal state of the resource, but to develop an optimal management capacity". He proposes four traditional approaches to management on which adaptive management builds:

  1. political/social approach
  2. conventional-wisdom approach
  3. best-current-data approach
  4. monitor-and-modify approach.

Johnson's approach is based on the recognition of the imposition of the limits to management. Once we recognise that there is no single, optimal state based either on ecological equilibria or social expectations he proposes, "we manage within a range of acceptable outcomes while avoiding catastrophes and irreversible negative effects". By focusing on smaller, replicated case studies across a region there are opportunities to develop general principles and guidelines that can be applied broadly to similar problems with local managers and stakeholders applying site specific modifications.

There are, however, recognised problems with adaptive management as a tool (as for every tool) with one of the key elements being trust. Stakeholders often play along with the process until they feel threatened or will make commitments they simply do not intend to keep. Some of the issues are not exclusive to environment management and are simply a reflection of social interaction. Work with alternative dispute resolution (ADR) shows that unless there is a genuine overlap of interests genuine commitments are unlikely to be achieved. In ADR this can be handled in private conversation with the parties, including clarification of how much each party is prepared to have revealed to the others of their position.

There is now a considerable literature that makes explicit the links between management and monitoring. This literature has, in many cases, been influenced by the lessons learnt from business where there often needs to be a more transparent link between objectives, alternative actions, and monitoring of decisions to improve subsequent decision-making. Much environmental monitoring seems to be divorced from management and promoted because it is a "good thing". The explicit links between objectives, risk and the choices between alternative actions are discussed above.

Monitoring is integral to (adaptive) management because monitoring provides the feedback which management requires to adapt, change, respond to the consequences of its actions.

A way of complementing large-scale adaptive management experiments is the use of meta-analyses, where published data can be used or a combined analysis of co-ordinated smaller scale experiments using the raw data for more powerful tests than for the single experiment alone.


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