Adolfo López-Paredes

Simulating the dynamic scheduling of project portfolios

Abstract A main challenge in project management is to provide methodologies that facilitate coordination among the projects in a portfolio or in a firm. Each incoming project in an existing portfolio affects its schedule, the resources availability and the planned performance. There are no analytical solutions for the problem of dynamic scheduling of resources for multiple projects in real time. Mathematical approaches, like integer programming or network based techniques, cannot describe the complexity of real problems (multi-projects environments have many interrelated elements), and have difficulties to adapt the analysis to dynamic changes. We propose a multi-agent system, where projects negotiate the procurement of resources through an auction mechanism all over the portfolio life. Both, projects and resources are modelled as agents. Projects demand resources for fulfilling their scheduled planned work, whereas resources offer their capabilities and workforce. This framework allows project portfolio management and the assessment in the decision of acceptance/rejection new projects.

Urban Water Management with Artificial Societies of Agents: The FIRMABAR Simulator

In this article, the authors present an agent-based simulator for the integrated freshwater assessment in a geographical area. It has been developed and successfully applied to the metropolitan region of Barcelona, and it is currently applied to the metropolitan area of Valladolid. Such simulator provides the policy makers with an additional tool to evaluate alternative water policies in different scenarios. The design and the validation of the model are based on participatory processes with a platform of the representative stakeholders.

Towards a new experimental socio-economics: Complex behaviour in bargaining

Game theory has provided a rigorous conceptual support to analyse strategic decisions and bargaining behaviour. But it shares with competitive equilibrium three basic assumptions. The players are fully rational; they comprehend the faced situation; and they know all the relevant institutional parameters. Thus disregarding the social dimension of bargaining. In this paper we advocate for a consilient focus of economics, psychology and sociology. We argue that it is necessary and possible to experiment within a Multi-Agent Systems (MAS) with bounded rational agents endorsed with explicit social behaviour. The observed behaviour is obtained from a laboratory experiment with human agents and can be captured in a cognitive multi-agent modelling with artificial agents. The approach can accommodate both declarative and procedural rationality; i.e., rationality as a process and as a product of learning.

Strategic Behaviour in Continuous Double Auction

We analyze with a bottom-up approach the competition between artificial intelligent agents in Continuous Double Auction markets in terms of allocative efficiency, price convergence and emergence or not of Nash equilibriums. In previous works agents have a fixed bidding strategy during the auction, usually under symmetric environments. In our simulations we allow the soft-agents to learn not only about how much they should bid or offer, but also about possible switching between the alternative strategies. We examine the behaviour of strategic traders under general supply and demand schedules (asymmetric environments) thus extending previous results.

Mesoscopic Effects in an Agent-Based Bargaining Model in Regular Lattices

The effect of spatial structure has been proved very relevant in repeated games. In this work we propose an agent based model where a fixed finite population of tagged agents play iteratively the Nash demand game in a regular lattice. The model extends the multiagent bargaining model by Axtell, Epstein and Young [1] modifying the assumption of global interaction. Each agent is endowed with a memory and plays the best reply against the opponents most frequent demand. We focus our analysis on the transient dynamics of the system, studying by computer simulation the set of states in which the system spends a considerable fraction of the time. The results show that all the possible persistent regimes in the global interaction model can also be observed in this spatial version. We also find that the mesoscopic properties of the interaction networks that the spatial distribution induces in the model have a significant impact on the diffusion of strategies, and can lead to new persistent regimes different from those found in previous research. In particular, community structure in the intratype interaction networks may cause that communities reach different persistent regimes as a consequence of the hindering diffusion effect of fluctuating agents at their borders.

Application of Model Driven Techniques for Agent-Based Simulation

Agent-based simulation is being recognized as a useful tool for the study of social systems. It is based on the idea that agents can be used as a good abstraction of members of a society, and by simulating their interactions, observe the emergent behavior. Usually, agent-based models for social simulation are rather simple, but there are more and more works that try to apply this technique for rather complex systems, both in the typology of the agents and their relationships, and in scalability. For this reason, software engineering techniques are required to facilitate the development of agent-based simulation systems. In this sense, it can be useful to consider agent-oriented methodologies, as they cope with these requirements. This work explores the application of a model-driven methodology for the development of MAS, INGENIAS. There are several advantages of this approach. One is the possibility to define specific modeling languages that are conceptually close to the domain-expert, in this case for simulation of social systems. This will facilitate the communication of multidisciplinary teams and, by generating code from models, the social scientist is alleviated from the concerns of programming for a simulation platform. This is illustrated with a case study of urban dynamics.

Evolution of Equity Norms in Small-World Networks

The topology of interactions has been proved very influential in the results of models based on learning and evolutionary game theory. This paper is aimed at investigating the effect of structures ranging from regular ring lattices to random networks, including small-world networks, in a model focused on property distribution norms. The model considers a fixed and finite population of agents who play the Nash bargaining game repeatedly. Our results show that regular networks promote the emergence of the equity norm, while less-structured networks make possible the appearance of fractious regimes. Additionally, our analysis reveals that the speed of adoption can also be affected by the network structure.

An Artificial Economics View of the Walrasian and Marshallian Stability

The experiments discussed below are an attempt to examine two concepts of instability which stem from two different models of market adjustment used in Economics: Walrasian (W) and Marshallian (M) instability. The M model views volume as adjusting in response to the difference between demand price and supply price at that volume. The W model views price as changing in response to excess demand at that price. Do the M and the W models have a firm foundation on micro-motives, or are they just macro abstractions that we could dispense of in Microeconomics?

Learning in Continuous Double Auction Market

We start from the fact, that individual behaviour is always mediated by social relations. A heuristic is not good or bad, rational or irrational, but only relative to an institutional environment. Thus for a given environment, the Continuous Double Action (CDA) market, we examine the performance of alternative intelligent agents, in terms of market efficiency and individual surplus.

Modeling of a Nuclear Emergency Plan: Communication Management

ABSTRACT The Nuclear Emergency influence domain spreads over a large number of systems of different natures. Nuclear Emergency Plans (NEPs) are usually designed using a top-down approach, establishing a hierarchy working as a command chain. Although this provides advantages in terms of control, it might not be flexible enough to face unpredicted emergent behaviors due to complex interactions among the systems involved. Using a real case study, this work models the organizational framework defined in a NEP and analyzes its communication and command chain network structure. We also discuss the results of the communication and command chain network analysis to facilitate the assessment of key indicators to improve the NEP: resilience, adaptability, and responsiveness.