Experiments regarding the example of transportation included the base scenario (without any intervention) and three governance scenarios with different degrees of intervention. The latter three focus on cars with internal combustion engine and take for granted (and refrain from debating in detail) that this technology harms environment more than other technologies, e.g. by its CO2 emissions. These three governance scenarios were implemented as follows:
- Soft control: via road pricing, limited in space and time. Costs of agents using the car are stepwise raised, if traffic jams occur or pollution exceeds limits, and afterwards lowered again.
- Strong control: via spatial and temporal bans of cars, if another limit is reached. Agents are forced to change technology or take another route.
- Combination of soft and strong control.
The effects of interventions were measured by means of various indices:
|Scenario||Mean capacity utilization on edges*||Mean emission short-time*||Mean emission long-time*||Bicycle usage||Car usage||Public transport usage|
|Self-coordination (basic scenario)||21,36%||17,96%||33,28%||31,61%||62,45%||5,94%|
As the table shows, we achieve desired effects with all three modes of governance: a decrease of car use and an increase of bicycle and public transport usage, and – triggered by these changes – also a reduction of capacity utilization and emissions. Additionally, as red number show, soft control mostly performs best (or closely second best).
Impact of Governance Scenarios
The left figure below demonstrates again the varying impact of different modes of governance on technology usage: The base scenario remains stable over 8000 ticks, as does the strong control scenario, where values adjust after a few ticks rapidly to a level that doesn’t change any more in the long run, but fluctuates in the short run. Soft control has the highest impact in terms of long-term learning. In spite of short-term fluctuations, agents change their behaviour remarkably in the long run in favour of more sustainable modes of transport. Surprisingly, the figure of the combination of soft and strong control looks so similar to soft control only, that one could recommend refraining from strong measures as a means of regime change.
The right figure, showing the population, may offer a possible explanation for the sudden switch at about 1000 ticks. At this point of time the composition of the agent-population suddenly changes. Agents insisting to use the car even in high-price situations (provoked by soft measures such as tolls) run into financial problems and die out. As a result of this stubbornness, the learning algorithm on population level (see Decision Making) triggers/pushes a slow, but gradually accelerating change in proportions of agents first and then – as a consequence – of technologies’ usage as well. Again, this mechanism can be seen most clearly in the soft control scenario.
Summarizing these results, one could argue that soft control is the most effective mode of governance, leading mostly to best results with lower efforts, compared to the combination of soft and strong control.