Sensitivity

One question I may ask specific to my DES model would be: how does increasing and decreasing the number of decisions work items make impact the results of my model? I explored a similar question for my SD model when I asked how the model changed when individuals flowed through the states (and so changed opinions) at a faster rate. My base model had most work items arrive at the decision point roughly according to an exponential distribution with a mean of one year. Although this mean generally reduced throughout the 4 year process according to the current NGO and Partnership activity. I explored the change in behaviour when the base exponential mean was increased to two years (halving the av- erage number of decision points), or decreased to a half year (doubling the average number of decision points). I call these two cases slow and fast opinion changes respectively.

6.5a.

6.5b.

6.5c.

Figure 6.5: a, b and c show the public support for proceeding with the siting process over the 4 year lifespan of the proposal for slower opinion change. The vertical dashed lines indicate the start and end point of each stakeholder engagement period the partnership organise, and

I first explored how behaviour changes when individuals took twice as long to make a decision about changing their decision. In this case, the base exponential distribution had a mean of roughly 2 years (those holding a positive/negative state have a slightly lower mean than those holding stronger views). This distribution was still altered dependent on the current activity of the NGOs and Partnership resulting in individuals taking generally less time between decision points as the process continues. My results are shown in Figure 6.5.

I can see that my model has been quite sensitive to increasing the time between decision points. The public in all three communities now change their opinions far less quickly and are much less responsive to current events. In the final year of the process I can see some changes in opinion, particularly in increasing resistance to the proposal. Although, even for the ‘Rest of Cumbria’, the final support and resistance percentages were very similar (resistance to the proposal was increasing). The main reason to increase the time between decision points is to improve model runtime, however this is at the expense of reducing the public’s sensitivity to key drivers of opinion change. I concluded that, while I see similar trends to my original model, increasing the time between decision points for individuals within the system has reduced the sensitivity of my model far too much. Smaller increases could be considered, but there would be little benefit to doing this as run time of the model would not improve substantially.

6.4.1.2 Fast Opinion Changes

From my exploration of slower changing opinions, I asked the question of whether faster changing opinions could help improve model accuracy at the cost of model runtime. To explore this, I reduced the time between decision points to be sampled from base exponen- tial distribution with a mean of 6 months. As before this mean is adjusted according to the opinion state they enter, and the current NGO and Partnership activity within the model. In this case, I would expect individuals in the system to be far more sensitive to current events such as the lulls in Partnership activity between stakeholder engagement periods. My results are shown below in Figure 6.6.

6.6a.

6.6b.

6.6c.

Figure 6.6: a, b and c show the public support for proceeding with the siting process over the 4 year lifespan of the proposal for faster opinion change. The vertical dashed lines indicate the start and end point of each stakeholder engagement period the partnership organise, and

These results confirmed what I might have expected: that individuals change their opinions far quicker and are much more sensitive to current events. This seems to have the biggest affect between PSE1 and PSE2 where support is declining for all communities due to the activities organised by the NGOs when there is very little Partnership presence. However, the most variable part of the process is the time between Fukishima (after PSE2) and when the decision deadline extension request (after PSE3). This period showed the largest devi- ation between repetitions because of the strong feedback effect coupled with individuals in the system being far more sensitive to change. However, during the final few months the deviation of results starts to significantly decrease due to the very similar trend being shown in each run of my model.

If I was to decide to reduce the time between decision points in this way, I would also need to be careful to increase the chances of an individual keeping their current opinion. This would allow my model to simulate individuals being far more pro-active about updating their current beliefs of the siting process, while ensuring individuals are not changing opin- ions rapidly for the entire duration of the process. The comparison of these two adjustments suggests the question of including more individual differences between individuals. Some members of each community would seek information about the siting process actively and would be best modelled by the faster opinion change system, while others could demon- strate very little interest and so would be better represented by a slower opinion change system. This style of modelling would be possible in a DES model such as this, but would be best suited to an agent-based model. This would be a model I would like to pursue should I be able to collect more data on individual responses rather than large-scale survey results of overall support.

6.4.2 Deadline Extension

As in my SD model, I asked the question of whether the deadline extension near the end of the siting process had unfairly biased the final vote. My SD model showed that, had the Partnership been allowed to continue their activity and enter a fourth stakeholder engage- ment phase, PSE4, then the outcome may have been more positive. In particular, while support was not growing for the proposal, its decline had been reduced considerably. This would have significant impacts on the council’s final vote as they would be under less pres- sure from the now relatively stable public (although the councils were not directly modelled in the DES model due to difficulty in validation of results). I considered the question: does this behaviour still happen in my DES model? I leave the discussion of the differences between my SD and DES models to Chapter 7.

6.7a.

6.7b.

6.7c.

Figure 6.7: a, b and c show public support for proceeding with the siting process when the Partnership is allowed to continue operation after the deadline extension. Vertical dashed lines indicate the start and end points of each stakeholder engagement period, and diamonds

To test this question, I modelled the partnership continuing their activity past the deadline extension and entering PSE4 as I defined for my SD model. To keep comparability between my models, I defined PSE4 for my DES model similarly to in my SD model by keeping partnership activity and events consistent between models. The key factors I looked for to answer this question was how public opinion changed near the end of the process, in com- parison to the results shown in Figure 6.4. In particular, I are interested in the differences in the last several months of the process (as the behaviour in early years of the process has not changed). These results are shown in Figure 6.7.

The inclusion of PSE4 has had a positive affect on public opinion in comparison to the baseline scenario shown in Figure 6.4. My SD model showed that PSE4 held public sup- port steady at the end of the process. However, my DES model showed that resistance grows even with PSE4. Despite this, I can still see that the increase in resistance is far slower than in my original model, seen in Figure 6.4. These difference likely stems from how PSE4 was defined in each model (for example general NGO activity grew faster in my DES model nearer the end, while my SD model had a more consistent high activity state for the NGOs), and from the differences between individuals behaviour. Including PSE4 in my SD model had a forced impact on the number of people in each stock, whereas in my DES model the difference is only felt by those that considered changing their opinion during the last few months of the siting process. If I allowed a faster change of opinion, as seen in Figure 6.6, then I see more similar behaviour to my SD model.