In the late sixties the Canadian psychologist Laurence J. Peter advanced an apparently paradoxical principle, named since then after him, which can be summarized as follows: 'Every new member in a hierarchical organization climbs the hierarchy until he/she reaches his/her level of maximum incompetence'. Despite its apparent unreasonableness, such a principle would realistically act in any organization where the mechanism of promotion rewards the best members and where the mechanism at their new level in the hierarchical structure does not depend on the competence they had at the previous level, usually because the tasks of the levels are very different to each other. Here we show, by means of agent based simulations, that if the latter two features actually hold in a given model of an organization with a hierarchical structure, then not only is the Peter principle unavoidable, but also it yields in turn a significant reduction of the global efficiency of the organization. Within a game theory-like approach, we explore different promotion strategies and we find, counterintuitively, that in order to avoid such an effect the best ways for improving the efficiency of a given organization are either to promote each time an agent at random or to promote randomly the best and the worst members in terms of competence.

We study a prototypical model of a Parliament with two Parties or two Political Coalitions and we show how the introduction of a variable percentage of randomly selected independent legislators can increase the global efficiency of a Legislature, in terms of both the number of laws passed and the average social welfare obtained. We also analytically find an "efficiency golden rule" which allows to fix the optimal number of legislators to be selected at random after that regular elections have established the relative proportion of the two Parties or Coalitions. These results are in line with both the ancient Greek democratic system and the recent discovery that the adoption of random strategies can improve the efficiency of hierarchical organizations.

The Peter principle has been recently investigated by means of an agent-based simulation and its validity has been numerically corroborated. It has been confirmed that, within certain conditions, it can really influence in a negative way the efficiency of a pyramidal organization adopting meritocratic promotions. It was also found that, in order to bypass these effects, alternative promotion strategies should be adopted, as for example a random selection choice. In this paper, within the same line of research, we study promotion strategies in a more realistic hierarchical and modular organization and we show the robustness of our previous results, extending their validity to a more general context. We discuss also why the adoption of these strategies could be useful for real organizations.

In order to analyse the behaviour of pedestrians at the very fine scale, while moving along the streets, in open spaces or inside a building, simulation modelling becomes an essential tool. In these spatial environments, in the presence of unusual demand flows, simulation requires the ability to model the local dynamics of individual decision making and behaviour, which is strongly affected by the geometry, randomness, social preferences, local and collective behaviour of other individuals. The dynamics of people visiting and evacuating a museum offers an excellent case study along this line. In this paper we realize an agent-based simulation of the Castello Ursino museum in Catania (Italy), evaluating its carrying capacity in terms of both satisfaction of the visitors in regime of normal fruition and their safety under alarm conditions. Motivation and OverviewWalking is the most sustainable mode of transport. Survey data from a selection of seven European countries shows that 12-30% of all trips is made by walking (OECD, 1998). In Italy it involves 75% of all trips under a kilometer, as reported by ISFORT (2006), it is the first and last segment of every travel, affects the level of service of important transport infrastructure such as airports and railway stations. At the same time it is also of fundamental importance in fields related to urban planning, emergency, disaster planning. On the other hand, transportation engineering is traditionally focused on motorized travel and therefore there is a general lack of research and methods to model pedestrian behaviour. Existing transport pedestrian models can be roughly separated in analytical models and micro-simulations.The first ones include "before and after" methods and regression analysis models (Older 1968, Pushkarev 1971, analogies with fluids, gas kinetics and other physical flow systems (Helbing 1992, Henderson 1974), entropy maximization (Butler 1978), dynamic network analysis with flow models calibrated on the basis of collected data (Di Gangi 2007), discrete choice models to predict pedestrians' route choice (Antonini 2006, Ignaccolo 2006, stochastic queuing and Markovian models (Mitchell 2001). In all these cases, the authors use mathematical models to calculate average pedestrian flows along a path, but these models are not able to include peculiar aspects of pedestrian (human) behaviour.The second ones simulate the movement of each single pedestrian following a set of predetermined rules of behaviour and are applicable to a greater variety of situations, such as closed spatial environments or unusual demand flows, where local dynamics of individual decision making is strongly affected by geometry, randomness, social preferences, local and collective behaviour of other individuals. Helbing (1995) proposed a simulation approach based on the concept of "social force", that includes a sort of internal motivations of the individuals to perform certain actions (movements) and its influence on people's dynamic variables (velocity, acceleration, distance). M...

Agent-based simulations show their potential in many context of transport management in presence of unusual demand, such as airport passenger terminals, railway stations, urban pedestrian areas, public buildings, street events or open space exhibitions, where management or control by related authorities and public safety are strongly affected by spatial geometry and crowd behavior. We illustrate these ideas with an example based on the simulation of people visiting and evacuating a museum, which offers an excellent test environment for simulating a collective behavior emerging from local movements in a closed space. The model we apply is developed within a programmable modeling environment, NetLogo, designed for simulating timeevolution of complex systems. We verify the existing emergency plan for building evacuation, for different demand patterns such as visiting group size and inter-arrival times, and we compare it with alternative evacuation strategies looking for the optimal one. In this respect, we further demonstrate the effectiveness of agentbased simulations in finding emergent results difficult to be predicted.

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