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They ask the very natural question of what the impact of Byzantine players on the performance of the system compared to either the purely sel sh setting (where all players are self-interested and there are no Byzantine players) or to the social optimum is To address such questions, they introduce the very interesting notion of the price of malice that captures the ef ciency degradation due to the presence of Byzantine players (on top of sel sh players) Moscibroda et al [72] use the price of malice to quantify how much the presence of Byzantine players can deteriorate the social welfare of the distributed system corresponding to the virus inoculation game of Aspnes et al [6] Most interestingly, Moscibroda et al.

most companies as benchmarks or experience may be unavailable. Subsequent audits can begin with the preceding audit and marketing plan. A few guidelines are given below to enable the preparation of a simple SWOT analysis. The SWOT should be concise and speci c, and communicate the key issues facing the rm. The items identi ed should be related to each other and not simply an ad hoc collection that does not point to future action. A clearly written analysis is helpful when it points to solutions or direct consequences. For example, A new outlet of a large competitor threatens our childrenswear department. Allow for a potential slowdown in sales turnover of 8 10% in next 12 months. The alternative for the above childrenswear department may be to change the marketing mix to meet the threat more directly. An important dimension of the SWOT analysis is the need for objectivity. This argument equally applies to the marketing audit. Fashion marketers charged with producing a marketing plan can sometimes be too close to the rm to be objective, as identifying certain weaknesses can be construed as admitting past failures. Outsiders, such as marketing consultants, can bring a broader and more objective perspective, but they are expensive and they take time to get to know the operations and procedures of the particular rm. At times, it can be most useful to employ a manager from another department or branch of the same company or a retired senior executive to bring objectivity coupled with company knowledge to help with the SWOT analysis.

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possible graphs are shown in Figure 7.8 (upper numbers). Obviously, graph 5 would be selected, which is indeed the graph that represents a decomposition of the distribution (cf. Section 3.4.2). Note that, as in the relational and the probabilistic case, the complete graph 8 always receives or shares the best assessment, because it consists of only one clique. As a consequence, it represents no real decomposition of the distribution. Note also that in order to rank two graphs (that is, in order to decide which of them is better) we need not compute the specificity divergences explicitly, but can confine ourselves to computing the nonspecificities of the distributions corresponding to the graphs, because the nonspecificity of the original distribution is fixed. This simplifies the assessment: find the graph among those of acceptable complexity (w.r.t. maximal clique size) for which the corresponding possibility distribution is most specific. Up to now we have assumed that the multidimensional distribution, for which a decomposition is desired, is given. However, as in the probabilistic setting, this is usually not the case in applications. Instead we are given a database of sample cases. Clearly, the situation parallels the probabilistic case: it is infeasible to estimate first the joint possibility distribution from the database, so that we can proceed as indicated above. In order to derive a feasible indirect method we may reason as follows: an approximation of a given possibility distribution may be compared to this distribution by integrating the differences in the number of tuples for each -cut over the possible values of (see above). However, this formula can be

#ifndef PLAYERSERVER_hh #define PLAYERSERVER_hh_ #include "example.hh" class PlayerServer : public virtual PlayerBOAImpl { private: char * the_player_name; public: PlayerServer(); virtual NameStr name(CORBA::Environment & env = CORBA::default_environment); virtual void set_name(char *); }; class PlayerFactoryServer:public virtual PlayerFactoryBOAImpl { virtual Player * createPlayer(NameStr name, CORBA::Environment & env = CORBA::default_environment); }; #endif ///////////////////////////////////////////////////////////// // PlayerServer.cc // 20 March 1999 ///////////////////////////////////////////////////////////// #include "PlayerServer.hh" #include <iostream.h> PlayerServer::PlayerServer(){ the_player_name=NULL; }; void PlayerServer::set_name(char * name){ if (the_player_name != NULL) delete the_player_name; the_player_name = new char [strlen(name)+1]; strcpy(the_player_name,name); cout << "Created Player object for " << name << "\n"; }; NameStr PlayerServer::name(CORBA::Environment & env = CORBA::default_environment){ char * ret = new char[strlen(the_player_name)+1]; strcpy(ret,the_player_name); return(ret); }; Player * PlayerFactoryServer::createPlayer(NameStr name, CORBA::Environment & env = CORBA::default_environment){ PlayerServer * aPlayer = new PlayerServer; aPlayer->set_name(name);

[72] demonstrate that in case the sel sh players are highly risk-averse, the social welfare of the system can improve as a result of taking Byzantine players into account! We expect that Byzantine game theory will further develop in the upcoming years and be applied successfully to evaluate the impact of Byzantine players on the performance of sel sh computer systems..

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