1.5 Chapter Review
2.2.3 Agent Based Simulation
This section provides a detailed review of the ABS paradigm, as it is the selected research method for this thesis. The following discussion details the historical evolution, under-
lying theory, positive aspects, limitations and software related to ABS. This review feeds into that of Section 2.2.4 which examines present advances in simulation theory and tech- nology.
History
ABS is said to have its origins in the work of von Neumann(1966), who wished to study
artificial automata. von Neumann(1966) developed a blueprint for a self-reproducing ma-
chine, which involved a series of complex governing rules and heavy-duty machinery. On the advice of a colleague, the complexity of the machine was stripped back and represented
by a Cellular Automata approach (Langton, 1997); the theory that global complexity can
emerge from simple local rules later becoming an important principle of ABS (Gleick,
1997).
A Cellular Automata approach interprets a system as a grid of cells, with the “state” of each cell represented by its own variable values. The system evolves in discrete time,
with the value of a variable at one cell having the potential to affect the values of adjacent
cells (Wolfram, 1983). The local actions of each cell causes the emergence of global
behaviour, leading to a “bottom up” approach for modelling a system (Heath, 2010); a
further important aspect of modern day ABS.
Cellular Automata were also used by Conway in “the game of life” (Gardner,1970). The
game of life takes place upon a square grid, whereby each cell is either dead or alive. Every cell impacts each of its eight neighbours - those cells which are vertically, horizontally or diagonally adjacent. The following rules govern the game:
• Living cells with four or more living neighbours die of overpopulation. • Living cells with fewer than two living neighbours die from isolation. • Living cells with two or three living neighbours remain alive.
• Dead cells with exactly three living neighbours become a live cell.
This is a zero player game whereby the initial conditions dictate the evolution of the system
cell setup and its evolution to a final steady state. Various patterns can evolve from the
selected initial conditions of the game (Adamatzky,2010).
Initial Conditions Stable ConditionsSteady State
Figure 2.2: The game of life, red represents a living cell and blue represents a dead cell -
images generated from NetLogo (Wilensky,1999).
A further piece of influential work in the formulation of modern day ABS (based upon
Cellular Automata) is that of the Schelling(1971) model. Schelling(1971) aimed to ex-
plore the effects of racial segregation in a community. A neighbourhood of houses was
represented as a grid of cells, each cell being one of two possible types (representing two
different racial backgrounds). The cells had a predefined level of tolerance to neighbour-
hood integration; if racial diversity in a cell’s neighbours rose beyond a tolerable level, the cell could change position on the grid. The model demonstrated that even if agents had a small preference to their neighbours being of the same racial background, then segrega- tion would occur. While initially no computer simulations were used in the creation of the segregation model, the ideas are said to be at the very foundations of ABS.
Cellular Automata have since been used to represent all manner of systems, including
clouds (Nagel & Raschke, 1992), forest fires (Hernández Encinas et al., 2007) and HIV
infection (Mo et al., 2014). However, the work of Reynolds (1987) marked a particular
turning point in the development of ABS, through the creation of a model to represent
Automata, allowing agents to inhabit 3-dimensional space - modern day ABS are able to represent agents on a grid, in 3-dimensions or in an environment where spatial capacity is irrelevant.
The historical development of ABS described herein only presents one facet of its evo-
lution, with Epstein (1996) stating that ABS also draws upon cybernetics, connectionist
cognitive science, distributed artificial intelligence, genetic algorithms and genetic pro- gramming. A wider perspective of the historical development of ABS may be found in
Heath (2010). Throughout the literature presented, a resounding concept emanates: the
actions of autonomous agents drives some emergent system behaviour; a discussion of how this is formulated in a simulation paradigm requires an analysis of the underlying theory of ABS.
Theory
ABS is a micro-simulation technique (Davidsson, 2001), which aims to model the indi-
vidual behaviours of specific objects in a system. Agent Based Models are sometimes
referred to as Individual Based Models in the study of ecology (Grimm et al., 2006), or
Multi Agent Systems (MAS) - a term adopted predominantly in engineering. MAS is said
to differ from ABS in that the focus is upon the development of operational agents to in-
form real world agents, as opposed to ABS, where the goal is to create agents which lead
to an understanding of global phenomena (Niazi & Hussain,2011).
There are three main components of ABS theory:
• Agents - The objects in the system being modelled are the agents. There is no
commonly agreed precise definition of an agent, but Huhns & Singh (1998) state
that “agents are active, persistent (software) components that perceive, reason, act
and communicate”. Davidsson (2001) expands this further, suggesting that agents
may posses any or all of the following qualities to varying degrees:
◦ proactiveness - reactions to the behaviour of other agents, or preventative ac- tions to avoid certain situations;
◦ spatial explicitness - the awareness of the spatial plane inhabited; ◦ mobility - the ability to move amongst the spatial plane;
◦ adaptivity - the ability to learn and/or change behaviours;
◦ modelling concepts - the consideration of personal beliefs, desires and inten- tions.
An ABS generally contains a large number of agents, contained within some envi- ronment, performing decisions or tasks predetermined by the modeller. An agent may be used to represent any object or entity, and the environment need not be spa- tial in nature. The specific qualities possessed by the agent depend upon the entities
being modelled and the requirements of the simulation. Drogoul et al. (2003) ar-
gues that often an ABS does not possess any of the idealised properties described
above; an ABS offering a convenient way to represent autonomous agents, without
the agents themselves being remotely autonomous.
• Emergence - Emergence occurs when interactions at one level, give rise to be- haviour at another level - requiring new categories of description that are not ac-
counted for by the underlying components (Gilbert & Troitzsch, 2005). Within an
ABS, this refers to the interactions and behaviours of the implemented agents caus-
ing some emergent behaviour, driving the overall system evolution (Macal & North,
2005).
An example of emergent behaviour is a standing ovation following a theatre perfor- mance. Some individuals may have particularly enjoyed the performance, deciding to stand and show their appreciation. However, not all individuals may be com- pelled to stand by the performance, but do so anyway as a result of the actions of those around them. Therefore, a standing ovation emerges as a result of the actions
of a number of individuals and the responses of those around them (Miller & Page,
2004).
• Complexity - A complex system results from the non-linear interactions of its con-
stituent parts (Mitchell, 2009). It is a system in which there are organised but un-
haviours and interactions appear greatly complex when viewed from a macro per-
spective (Miller & Page,2007). The characteristics of a complex system at any one
time may be directed by its history, or rather, the history of its constituent elements
(Gilbert,2004). In an ABS, the complex system is represented in terms of its agents,
the agent being the primary focus of the modelling paradigm.
The perspective of an ABS is in stark contrast to SD (which takes a global view of the system) and provides a representation of entities which is not captured by DES. Further
information regarding the difference between the simulation methods, may be found in
Borshchev & Filippov(2004), withSiebers et al.(2010) giving a clear distinction between
the entities of DES and ABS models. Simulation method selection is dependent upon the system being investigated, with each method yielding specific benefits. A discussion of the specific benefits of an ABS now follows.
Benefits of ABS
Aside from the benefits inherent with adopting a simulation approach in general (previ-
ously discussed in Section 2.1.1), Bonabeau(2002) states there are three specific advan-
tages of selecting an ABS approach:
• Modelling Emergence - Many systems can be characterised by the emergent be-
haviour of its entities (Gilbert & Troitzsch, 2005;Regenmortel,2004;Suweis et al.,
2013). Emergent phenomena would be difficult to capture with alternative methods,
making ABS the canonical approach to modelling such systems (Bonabeau,2002).
Furthermore, studying the behaviour of agents allows for an investigation of the re-
lations between agents (O’Sullivan,2004), which may also be an important factor of
system evolution.
• Natural Description - ABS may provide a more natural view of a system. For example, the dynamics of pedestrians in a shopping centre may be more naturally expressed by individualistic decisions, as opposed to an overarching system dictating footfall. With regard to a population, aggregate approaches generally assume homo- geneous mixing; however, there may be situations where an amalgamated view of a
population is inappropriate (Axtell, 2000). For instance, in the spread of a sexually
of disease transference. ABS is also said to be more intuitive than other approaches, especially when examining business processes. An abstract SD model of business
flow may be not be intuitive for managerial staff and stakeholders, however, observ-
ing the process from an individualistic viewpoint may be simpler to conceptualise
(Bonabeau,2002).
• Flexibility - ABS offers a flexible framework to work with active entities. Agents
can be added or removed from the system with ease, with the decision processes of agents capable of being highly simplistic or incredibly complex - as per the require- ments of the model. ABS is also particularly useful when agents inhibit a geospatial
platform (Axtell,2000), with the ability to represent an environment as a discrete or
continuous field (Helbing & Balietti,2012).
ABS is also said to allow for detailed hypothesis testing, as the focus is upon specific
aspects of an agent (Helbing & Balietti, 2012). As such, an ABS is most appropriate
for domains characterised by a high degree of localisation (Parunak et al.,1998), whereby
local actions impact the global system. While agents are a useful vehicle for understanding
complex and non-linear systems (Ferber, 1999), there are also a number of limitations to
consider. Limitations
Many of the limitations discussed within the ABS literature relate to simulation methods in general, such as suitability, validity and detail. This may be particularly amplified with
respect to ABS, as the individualistic processes of an agent may be difficult to quantify.
Castle & Crooks(2006) argue that the surprising and counter-intuitive behaviours emerg-
ing from an ABS are rarely encountered in the real world, withCouclelis(2002) claiming
that an ABS is sensitive to initial conditions and small variations in interaction rules. A fur-
ther difficulty is the actual development of agents, with structural and decisional autonomy
being difficult to achieve (Drogoul et al.,2003).
Axtell (2000) states that for an ABS to develop robust conclusions to theories, multiple
runs are necessary - a result of the emergent behaviour potentially varying with the initial conditions selected. This may require a great deal of computational power, as agents are
technological developments and distributed simulation approaches (discussed further in Section 2.2.4). Sophisticated ABS software packages are also available to aid in the design of a model, a brief discussion regarding ABS software may be found below.
Software
There are five main software platforms generally discussed in the development of an ABS:
• NetLogo (Wilensky,1999);
• Repast(2013),
• SWARM(2012),
• MASON(2012),
• AnyLogic(2002).
These may be classified into two groups: Open Source Systems and Proprietary Systems. An Open Source System (OSS) is generally freely available, with access to the source code permitted. In terms of ABS, this is usually in the form of a toolkit, providing the appropri- ate libraries and routines to develop a model; there are said to be over one hundred toolkits
available for ABS (Castle & Crooks,2006). A Proprietary System (PS) is a software plat-
form generally developed by an organisation who controls its licensing, with access to the source code strictly prohibited.
A brief introduction, and the positive and negative aspects of each platform, are as follows: • NetLogo (OSS) - NetLogo is a high level toolkit which implements its own pro- gramming language to develop a model, whereby agents are referred to as turtles. NetLogo is programmed procedurally and does not adopt an object-oriented frame- work, with the software said to be highly accessible to modellers with little pro-
gramming experience (Zhou et al.,2009). While a wealth of support documentation
is provided, along with a vibrant online help community, NetLogo is limited in terms of functionality - although extension is possible through Application Programming
the model discussed in Chapter 4.
• SWARM (OSS) - SWARM is a multi-agent platform developed predominantly for
the investigation of complex biological systems (Minar et al., 1996). The platform
is one of the earliest toolkits and at the time was said to be widespread and well
known amongst the agent community (Hofmann & Carole, 2004). SWARM pos-
sesses moderate functionality and some demonstration models are also provided;
however, Najlis et al. (2001) states that SWARM has a steep learning curve and
requires an experienced programmer for effective use of the toolkit.
• Repast (OSS) - Recursive Porous Agent Simulation Toolkit (Repast) is available
in three different programming languages: Java, Microsoft.Net and Python; how-
ever, new developments are solely released for the Java version (North et al.,2005).
Repast is tailored to the development of social systems and contains a point and
click GUI (Graphical User Interface) to aid model development (Railsback et al.,
2006). Although the platform boasts an active online support community, accessi-
bility for an inexperienced modeller can be problematic and documentation is often incomplete.
• MASON (OSS) - developed at George Mason University and based on Java. Zhou
et al.(2009) states that while MASON has good extensibility, modularity and porta-
bility, its capabilities are not as comprehensive as other platforms, possessing little technical documentation and the requirement of a proficient programmer to develop a model.
• AnyLogic (PS) - developed by XJTechnologies, it supports the creation of ABS, DES and SD simulations. The system is based on Java, meaning that although the software and development framework cannot be shared without a licence, the self- contained simulations may be exported and demonstrated on unlicensed machines. AnyLogic benefits from powerful modelling capabilities, an intuitive interface and a
professional support service (for a fee) (Zhou et al., 2009); however, specific capa-
bilities depend on licensing agreements and only a small online community support the software in comparison to other platforms. AnyLogic is used in this thesis in the development of the simulation discussed in Chapter 6.
More detailed comparisons of each platform may be found in Castle & Crooks (2006),
Nikolai & Madey (2009) and Zhou et al. (2009), assessing the software against differing
criteria and from various perspectives. AnyLogic has a unique selling point in that it allows for the creation of simulations in ABS, DES and SD frameworks, and permits the development of simulations combining the methods. Such hybrid models are becoming a growing topic of interest amongst simulation literature. Further discussions regarding hybrid models, and technological advances in simulation, may be found in Section 2.2.4.