There are two aspects of CREDIT to be tested (as discussed in section 5.3): (i) defining issues to be negotiated upon; and (ii) defining negotiation intervals. As was pointed out in section 5.3, CREDIT also performs partner selection based on trust. However, the behaviour of the model in this respect follows from the results of the other two mentioned above since these will also show how well CREDIT detects defectors and how fast the trust value changes accordingly. Therefore, to evaluate whether the CREDIT model does indeed bring added value to the agents, it is necessary to show how agents using the model can identify and cope with agents of different execution strategies26
with respect to enacting the contents of a contract.
In more detail, we will show how the trust model helps agents cope with other agents which (i) either defect (i.e. achieve the worst possible values for issues for their opponent) or cooperate (i.e. enact the contract) completely, and (ii) defect in degrees (i.e defecting from the values agreed to a limited extent). These execution strategies exploit the basic moves we described in the MMPD in figure 5.3 (see beginning of section 5.4). To this end we devise two sets of experiments with execution strategies as defined below27:
1. Experimental set 1 deals withextremedefection or cooperative execution strategies (i.e. with maximum defection or cooperation degrees or both):
(a) philanthropic (P) – never defects, and always delivers what has been agreed in the contract.
(b) nasty (N) – always defects maximally and achieves whatever brings it maxi- mum expected utility.
(c) tit-for-tat (TFT) – defects when the opponent defects but cooperates fully on the first contract.
(d) strategic defector (STDefect) – defects and cooperates alternatively in an attempt to keep up its opponent’s trust on it, thus exploiting the latter. 2. Experimental set 2 deals with agents that have fixed degrees of defection. To this
end we define the degree defector (DDd) as a defector which defects from an agreed value by a degree d in the range d∈[0,1]. The valued represents the maximum fraction (of the range of values that the issue can take) that the agent will defect by.
These two experimental sets generally cater for behaviours commonly encountered in e-commerce. For example, degree defectors could represent inefficient companies, com- 26Here we distinguish an execution strategy as being the behaviour an agent adopts when enacting
the contents of a contract from a negotiation strategy that is used in negotiating the contract.
27We exploit those strategies commonly used in assessing trust models (since Axelrod’s experiments
plete defectors represent hackers or spammers while philanthropic and tit-for-tat agents represent well established companies or sellers (e.g. on eBay or Amazon).
In the remainder of the chapter, we will denote the strategy used by an agent by tag- ging the strategy identifier with the role of the agent. For example, P-SP denotes a philanthropic SP agent and N-SU denotes a nasty SU agent. Wherever we will need to test our results for statistical significance we will use ANOVA (ANalysis Of VAriance between groups) to analyse the means of samples of different sizes to ensure that our means indeed exhibit the properties we seek, and as a result to prove or disprove our hypotheses.
The same fuzzy sets applying over utility deviations are given to each agent to charac- terise the performance of an opponent’s issues of a contract.28 Specifically, the three
fuzzy sets Bad, M edium, and Good are defined using linear functions based on figure 5.1. The basic settings for these experiments are summarised in table 5.6 and the utility functions together with the weight each issue has in the overall utility of each type of agent are given in table 5.7.
θmin 50
No. of specified issues 4
No. of unspecified issues 2
Institution Rules defined as per table 5.5
Fuzzy Sets Bad, Average, Good
Level of Confidence of Risk 95%
Table 5.6: Basic settings of the experiments for sets 1, 2 and 3.
Uc, ωc Us, ωs Ul, ωl Utc, ωtc Uqos, ωqos Uusage, ωusage SP 200c ,0.05 1−12s,0.05 6l,0.1 1−tc,0.1 1−16q ,0.5 1−usage100 ,0.2
SU 1−100c ,0.5 4s,0.2 1−10l ,0.1 tc
90,0.1 q8,0.05 usage200 ,0.05
Table 5.7: Utility functions used in the experiments for SP and SU agents. Note that all agents of each type (SU or SP) have the same utility functions but may have
different execution strategies.
These weights were chosen such that the agents play the MMPD (see section 4.2). Moreover, more weight is given to ‘specified’ issues than to ‘unspecified’ ones given that we expect an agent to consider those specified issues as more important than the unspecified ones (since the former arealways negotiated). Finally, the calculation of the overall trust value for each type of agent is given in table 5.8. As can be seen, the SU agent weighs its trust in each issue respecting the order of the weight each issue has in 28We expect these fuzzy sets to be different for each agent in realistic applications. This difference
in perception (which fuzzy sets express) will matter whenever agents are meant to exchange reputation values. However, this is a feature which we do not use here since the reputation values are assumed to be available from the society and we wish to keep a focus on the analysis of direct interactions rather than delve into the topic of aggregation of reputation values. See (Ramchurn et al., 2004b) for a wider discussion on reputation and trust.
its utility function (and similarly for the SP agent). This assumes the agent will choose an opponent it trusts most on those issues it considers most important. In remaining
T(α, β) = 0.5×T(α, β, c) + 0.4×T(α, β, s) + 0.1×T(α, β, qos) T(β, α) = 0.5×T(β, α, tc) + 0.4×T(β, α, l) + 0.1×T(β, α, usage)
Table 5.8: Calculation of trust values for an SU agent αand an SP agentβ.
subsections we detail the different experiments performed to test CREDIT when used by agents with different strategies. The behaviour of CREDIT is not specifically tested for one shot interactions. In such cases, we expect CREDIT to use the reputation model connected to it (e.g. REGRET, SPORAS or HISTOS) to dictate what should the behaviour be (Ramchurn et al., 2004d). Moreover, in the one-shot interaction case where the only interaction partner available has a low reputation, an agent might choose to interact within the framework of an institution which guarantees all or most of the terms of contracts they make.