The next step in the use of innovative, dynamic and performance-based contracts for service delivery by contractors could be use of monetary incentives to stimulate self-regulation of the network. Because it is currently unclear how performance-based payments in network tenders can effectively encourage network members to coordinate their own operations, a serious game was performed that simulates road maintenance planning to study changes in decision making and the emergence of network coordination. The experiments show that monetary incentives influence decision making, but their effect may be opposite to their intended aim and can lead to a competitive network. It was, however, also found that this competitiveness is not shown in networks where members are familiar with each other. This leads to the conclusion that penalty-based incentive mechanisms probably interfere with self-regulation and that the social dimension of contractor collaboration is paramount to the success of network-based contracting of construction activities.

Planning under uncertainty poses a complex problem in which multiple objectives often need to be balanced. When dealing with multiple objectives, it is often assumed that the relative importance of the objectives is known a priori. However, in practice human decision makers often find it hard to specify such preferences exactly, and would prefer a decision support system that presents a range of possible alternatives. We propose two algorithms for computing these alternatives for the case of linearly weighted objectives. First, we propose an anytime method, approximate optimistic linear support (AOLS), that incrementally builds up a complete set of -optimal plans, exploiting the piecewise-linear and convex shape of the value function. Second, we propose an approximate anytime method, scalarised sample incremental improvement (SSII), that employs weight sampling to focus on the most interesting regions in weight space, as suggested by a prior over preferences. We show empirically that our methods are able to produce (near-)optimal alternative sets orders of magnitude faster than existing techniques, thereby demonstrating that our methods provide sensible approximations in stochastic multi-objective domains.