Disciplined Exploration of Emergence Using Multi-Agent Simulation Framework

In recent years the concept of emergence has gained much attention as ICT systems have started exhibiting properties usually associated with complex systems. Although emergence creates many problems for engineering complex ICT systems by introducing undesired behaviour, it also offers many possibilities for advance in the area of adaptive self-organizing systems. However, at the moment the inability to predict and control emergent phenomena prevents us from exploring its full potential or avoiding problems in existing complex systems. Towards this end, this paper proposes a framework for empirical study of complex systems exhibiting emergence. The framework relies on agent-oriented modelling and simulation as a tool for examination of specific manifestations of emergence. The main idea is to use an iterative simulation process in order to build a coarse taxonomy of causal relationships between the micro- and macro layers. In addition to the detailed description of the framework, the paper also discusses the corresponding verification and validation processes as important factor for the success of such a study

Following the Problem Organisation: A Design Strategy for Engineering Emergence

To support the development of self-organising systems, we explain and rationalise the following architectural strategy: directly mapping the solution decomposition on the problem organisation and only relying on the problem abstractions for the design. We illustrate this with an example from swarm robotics

Methodological Guidelines for Engineering Self-organization and Emergence

The ASCENS project deals with the design and development of complex self-adaptive systems, where self-organization is one of the possible means by which to achieve self-adaptation. However, to support the development of self-organising systems, one has to extensively re-situate their engineering from a software architectures and requirements point of view. In particular, in this chapter, we highlight the importance of the decomposition in components to go from the problem to the engineered solution. This leads us to explain and rationalise the following architectural strategy: designing by following the problem organisation. We discuss architectural advantages for development and documentation, and its coherence with existing methodological approaches to self-organisation, and we illustrate the approach with an example on the area of swarm robotics

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Software agents in support of human argument mapping

This paper reports progress in realizing human-agent argumentation, which we argue will be part of future Computer-Supported Collaborative Argumentation (CSCA) tools. With a particular interest in argument mapping, we present two investigations demonstrating how a particular agent-oriented language and architecture can augment CSCA: (i) the use of the IBIS formalism enabling Brahms agents to simulate argumentation, and (ii) the extension of the Compendium tool by integrating it with Brahms agents tasked with detecting related discourse elsewhere

Oil scenarios for long-term business planning: Royal Dutch Shell and generative explanation, 1960-2010

Most executives know that overarching paints of plausible futures will profoundly affect the competitiveness and survival of their organisation. Initially from the perspective of Shell, this article discuses oil scenarios and their relevance for upstream investments. Scenarios are then incorporated into generative explanation and its principal instrument, namely agent-based computational laboratories, as the new standard of explanation of the past and the present and the new way to structure the uncertainties of the future. The key concept is that the future should not be regarded as ‘complicated’ but as ‘complex’, in that there are uncertainties about the driving forces that generate unanticipated futures, which cannot be explored analytically.oil scenarios; Shell; ACEGES; agent-based computational economics

Multi-Agent Based Modelling of an Endogenous-Money Economy

We present an agent-based model of a simple endogenous-money economy. The model simulates agents representing individual persons who can work, consume, invent new products and related production technologies, apply for a loan from the bank and start up a business. Through the interaction of persons with the firms, we simulate the production of goods, consumption and labour market. In order to achieve a significant level of realism of the simulations, the firms are modelled as adaptive agents using an effective reinforcement learning approach in continuous space. This setting allows us to explore how an endogenous-money economy can be built up from scratch, as an emergent property of actions and interactions among heterogeneous agents once money is injected into a non-monetary self-production (or barter) economy. In the paper, we first empirically investigate the learning capability of the firm agents. Then, we discuss the results of some computational experiments under different significant scenarios

Multi-Agent Based Modelling of an Endogenous-Money Economy

We present an agent-based model of a simple endogenous-money economy. The model simulates agents representing individual persons who can work, consume, invent new products and related production technologies, apply for a loan from the bank and start up a business. Through the interaction of persons with the firms, we simulate the production of goods, consumption and labour market. In order to achieve a significant level of realism of the simulations, the firms are modelled as adaptive agents using an effective reinforcement learning approach in continuous space. This setting allows us to explore how an endogenous-money economy can be built up from scratch, as an emergent property of actions and interactions among heterogeneous agents once money is injected into a non-monetary self-production (or barter) economy. In the paper, we first empirically investigate the learning capability of the firm agents. Then, we discuss the results of some computational experiments under different significant scenarios

Computation in Economics

This is an attempt at a succinct survey, from methodological and epistemological perspectives, of the burgeoning, apparently unstructured, field of what is often – misleadingly – referred to as computational economics. We identify and characterise four frontier research fields, encompassing both micro and macro aspects of economic theory, where machine computation play crucial roles in formal modelling exercises: algorithmic behavioural economics, computable general equilibrium theory, agent based computational economics and computable economics. In some senses these four research frontiers raise, without resolving, many interesting methodological and epistemological issues in economic theorising in (alternative) mathematical modesClassical Behavioural Economics, Computable General Equilibrium theory, Agent Based Economics, Computable Economics, Computability, Constructivity, Numerical Analysis

Disciplined Exploitation of Emergent Properties

Digital systems are becoming increasingly complex, requiring significantly more effort and resources in order to be designed, implemented, and maintained. In the last decade, industry and academia alike share the concern that in the near future engineers will have to face unprecedented levels of complexity. Similarly, the belief that traditional engineering approaches will be insufficient for coping with systems of such complexity is gaining increasingly more supporters. An alternative approach suggests the use of implicit engineering techniques which could lead to complex global-level behaviours by focusing solely on the local, or individual, level. The quality of rising macroscopic behaviours which are irreducible, or non-trivial to reduce, to any microscopic properties is more widely known as emergence, especially in the fields of complex and multi-agent systems. This work aims to investigate the possibility of engineering systems which harness, in intentional and disciplined ways, beneficial emergent properties. An experimental framework is being proposed to assist system designers towards that goal. This framework is based on the results and experience gained by Paunovski during the design of the Emergent Distributed Bio-Organisation (EDBO) case study. EDBO has demonstrated a number of beneficial emergent properties rising out of simple, bio-inspired, local interactions. The original implementation of the EDBO case study is closely coupled with a custom simulation platform; both developed by the same author. This work provides a basis for separating the EDBO case study from this combined implementation, by documenting it concisely and defining it in a formal manner. This formal model allows for rigorous testing and enables other authors to reuse the EDBO principles in their systems. The model is validated informally through animation and it serves as the basis of an independent implementation which cross-validated many of EDBO's original findings