The Scenario approach we discussed in the lecture and the seminar is used in part as system assessment tool (impact matrix) and as assessment tool for consistency of visions. Originally, it was presented in full length in the Book "Embedded Case Study Methods" by Roland Scholz and Olaf Tietje (2002).
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In the Lake Ordeal example: given the description above, we derive a quick and dirty set of possible impact factors.
Table 1 - Impact Factors and future states
This is first try to find some relevant impact factors. Note that the column on plausible future state corresponds to Step 3-1. Literature has it, that in a real case study you end up with around 20 impact factors from which a smaller set of around 12 are selected for scenario construction. Having many impact factors makes the actual scenario construction and consistency analysis computationally expensive and time consuming.
Selection of relevant impact factors is facilitated by the impact assessment, where usually the most active factors are chosen.
2-2 Impact assessment and 2-3 impact analysis
in General: for a meaningful construction of scenarios we need a thorough understanding of system strcture and dynamic. This knowledge is usually revisited when the final set of impact factors are selected for scenarios construction, at the consistency analysis and in the description and interpretation of scenarios.
We analyse impacts between impact factors by starting at row one and asking in our case, “does the state of the fish stock as described directly influence export rate?” If there is influence we insert a “1” and a “2” for a strong impact. In practice, you need to operationalize well, so detailed expert knowledge translates in a meaningful matrix. It is important, that we ask for direct impacts only. Going this direction, we have the sum of impacts in each row as the activity that the factor of the row exerts on other factors. Vice versa, the column sum is the passivity score of the respective factor.
In the Lake Ordeal example: In our case, the export rate is the most active factor, with the biggest influence on other system elements. Societal well-being is the least active and the most passive factor. As it is of major interest as a kind of target variable in our problem, we would still want to keep for scenario construction.
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Note that this example is only supposed to illustrate the reasoning that can lead to a limitation of factors. In reality this is hopefully backed by expertise on the respective subject matter.
3-2 Consistency assessment (see 3-1 future levels in 2-1)
In General: Now, for each possible future level of an impact factor, there will be a column and a row. This matrix is similar to the impact matrix, but now, we are assessing the plausibility of co-occurrance of each possible pair of future level. This is on "co-occurrance only". And that'st the other difference: there is no direction in the assessment, which means, we'll only have to fill in one half. Going on with the example will clarify this.
In the Lake Ordeal example: As we have already done the future levels in table 1, we can proceed with the consistency matrix:
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The consistency matrix gives an overview of the logical coherence of two co-occurring future levels of an impact factor. For instance, in our little example we assume that job opportunities take the pressure off the fish stocks, because less people are fishing. But, these opportunities are “paid” for by export revenues which is determined by world market price. And we assume reasonable business and government, exporting less when prices are high (because we need less to reach the goal).
In General: For making the actual construction, we create a scenario for every possible combination of future levels of each impact factor. This is a combinatoric construction and is usually done by a machine. Dealing with four impact factors and eight related future levels is still quite manageable. But when you think of 12 factors with some having three future levels, you just can't do that by hand. In our case there are 3 factors with 2 levels which results in 23 = 8 Scenarios. But considering our 6, still superficial, impact factors from above and assume good systems knowledge and thus each having three possible future levels, we already end up with 36 = 729 Scenarios. Now, with that number it is clear that we need a systematic way to select scenarios. Which we start by getting rid of the inconsistent ones.
In the Lake Ordeal example:
We need to take the consistency value of each pair of impact factors in a scenario. For example: consistency of S3 = (high social.. & depl. fish.. = -1 ) + (high social.. & high jobs.. = 1 ) + (depl. fish & high jobs.. = -1 ) = -1. So this scenario is not logical. We would not select it. Later, we will actually filter out any scenario which contains an inconsistency at any place because one inconsistent combination is enough to render a scenario itself inconsistent.
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So here we have our two consistent scenarios. Due to the many inconsistency ratings in the consistency matrix, there are only two scenarios left, where there is no inconsistency at all: S1 and S8.
In General: normally there would be a some hundreds or thousands of scenarions. Basing the selection only on consistency values, we might still end up with very similar scenarios. This wouldn't tell us much about the actual width of the option space. We are interested in how different the future can look like, not in incremental differences between the most plausible futures. For this purpose there is another routine to calculate the reative difference between scenarios (which is coming sool)
In General: after all this number crunching, we need to get back to some sense making. Out of the hundreds of possibilities, what do the selected ones tell us? Can we tell a story, using all our systems knowledge, that is compelling and reveals som more insight on the possible path to that future? Can we articulate a common preference for one of the scenarios presented?
In the Lake Ordeal example: The two Scenarios above basically refer to two narratives: in S1, things are going quite well. Fish stock recovers, people are well and they have new jobs outside the fishing industry. In S8, there are only jobs in the fishing industry, which leads to further depletion of the fish stock and leads to persistent problems caused by overfishing.
Of course, this is simplistic and of little help as it only confirms our prior belief that jobs outside the fishing game is the solution. You can go about the analysis with more impact factors and a more sophisticated assessment of impacts and consistencies. But for now, the general workflow should be clear.
