Posts Tagged ‘ABM’

ABMs: representation, coherence, balance

Monday, January 30th, 2012

O’Sullivan’s article is a rather critical account of ABMs. The article states the issue of highly funded models, which are too sensitive to reveal the outcomes of or are too complex to be explained in journal articles (544). State-of-the-art findings will reach a specific audience, not the audience that they were intended for. Thus, if ABMs are social agents, representing social issues, we have one serious limitation. How will transparency, availability, and clarity be attained? Perhaps we should strive for balance in models between the relationship of agents with space, and how and where those agents are represented (545).

I found it difficult not to get lost in the definitions of ABMs. In particular, their accuracy, validity, and lucidity. Bonabeau differentiates ABMs from market models with advancing game theory, by taking the focus off of the ‘theory’ part. On the other hand, O’Sullivan finds ABMs to be simple and abstract, effective for researching theory implications. He goes on further to state that ABMs, as they stand, “cannot establish the truth of those theories” (546). How then, can the truth of those theories be established? Should we be concerned with the idea of theory? Or restructuring what we deem that a model is all together? The issues with regards to the way space and time are represented are being explored and that’s definitely a good thing. Despite all the setbacks, there is much potential in ABMs as they are exploring numerous fields. As long as limitations diminish, there is hope.

-henry miller

ABMs and MBMs

Monday, January 30th, 2012

The focus on Bonabeau’s article here is of agent-based models (ABMs) and market-based models (MBMs). A main difference between an ABM and an MBM is that the former focuses on individual behaviour while the latter deals with collective behaviour. Furthermore, ABMs are driven by the bottom-up approach (focus is placed on the individuals), in contrast to the top-down approach (focus is on the collective) of the classic MBMs.

“ABM captures that emergent phenomenon in a natural way” (7282). Is this much different than a general equilibrium MBM? The MBMs that are largely based on the neo-classical economics are founded on similar ideals of natural systems, such as laissez-faire economics and Adam Smith’s infamous ‘invisible hand’. Referring to another model, Bonabeau states that “each agent acts individually but has perfect knowledge of how many users there are in the population” (7287). Whenever ‘perfect’ is utilized, it reminds me of the classical economic market systems where perfect competition, perfect information, and full employment are all assumptions made in assessing a market scenario with a standard economic model.

Bonabeau is at times too eager and possibly even blinded by his excitement of his tool: “ABM is perfect not just for operational risk in financial institution but for modeling risk in general” (7285). It is problematic to be this certain about a tool that is not fully understood, which he actually takes note of: if ABM is introduced in the market and is unsuccessful, potentially harming individuals with the predictions, or rather understanding (as the article emphasizes) of a situation, then the overestimated tool may do more damage than good. However, the article does mention this, but does not go into detail explaining why “agents behave in a way that is still poorly understood” (7284). Perhaps the statement “AMBs are more of a mindset than a technology” (7280)alludes to this problem. Can we change the modern market system with ABMs if we are aware of their positive and negative implications? Is it possible to create economic software agents that do not simply explain human economic behaviour?

-henry miller

How to Define Agent-Based Modeling in Human System Study?

Monday, January 30th, 2012

Agent-based modeling (ABM) has been widely utilized in social science as a powerful modeling tool in the past few years. However, it still remains difficult to conclude an accurate definition of ABM. When it comes to the study of human systems, the situation becomes more complicated due to the uncertainties in human society.

In this paper, Bonabeau conclude that ABM provides three important benefits for simulating human systems. First, it presents a natural description of the study target. Second, it is quite flexible and can be easily adapted for different dimension with the ability to learn and evolve by itself. Finally, ABM captures emergent, which is quite challenging and important in study of human systems. All these features have conceptualized ABM as a necessary tool to handle the complexity in human system studies.

I tend to define ABM as a collection of modeling methodologies, rather than a group of theories. ABM contains a wide body of disciplines, and it seems the number of disciplines will continue increasing. It relies on the methodologies and techniques of statistics, pattern recognition, reinforcement learning, control theories, system identification, game theories, and so on. However, ABM distinguishes itself from the mentioned disciplines by more emphasis on practical usage, not on the solid theory background. For example, ABM can model auction activities without the proof of Nash Equation’s existence, which is not acceptable in game theory studies. Moreover, ABM may utilize reinforcement learning as a tool for self-learning, without any theoretical proof of whether it can converge to the satisfying result or not. Therefore, I think ABM as a collection of practical modeling methodologies, and it will change its definition with different applications.


Utilizing ABM in GIS Research

Monday, January 30th, 2012

O’Sullivan gives a very good survey about the agent-based models (ABM) in spatial science research. He begins with different definitions of ABM, analyzing their advantages and disadvantages. Then he categorizes ABM applications into three types, with respect to their degree of complexity. As mentioned by Bonabeau, ABM provides an efficient approach to describe the complex systems. O’Sullivan illustrates this point in the paper by delineating spatiotemporal and social ABM representation methodologies. In geography research, ABM provides a powerful tool for modeling geospatial information on computers. However, the challenges should not be overlooked. As ABM represents geospatial information at individual level, the complexity and model verification are becoming more difficult with the increase of agent numbers.

By modeling interesting entities as agencies, ABM reviews the relationship and interactions between these agencies. Sometimes, the study targets in GIS research are in a great number and their relationship can be very complicated for statistical modeling. But ABM can achieve that by modeling each entity or each attribute of the study target, providing detailed models about the intricate phenomenon.

I propose that ABM should be integrated with other geospatial analytical methods in GIS research. By viewing a large number of agencies at aggregate level, we can find several interesting discoveries that cannot be reflected by studying the interactions between the agencies. For example, geospatial statistics can study climate changes at global level, by utilizing geospatial information provided by a large body of agencies which contribute to the climate change. Therefore, utilizing other analyzing tools with ABM can help us in GIS research.


A Bit More On The Appropriateness/Drawbacks of ABM

Sunday, January 29th, 2012

I know a couple of folks have posted on this topic, but I wanted to add my two cents given that both of our authors for last week treat the drawbacks of ABMs in some detail. In particular, I’m interested in David O’Sullivan’s idea that simple models are necessary in science in order to arrive at understandable explanations of what’s taking place within the model or with an emergent phenomenon (546). While understandable as a scientific paradigm, I think this approach may explain the sentiment captured in “Sidewalk Ballet’s” post and the subsequent debate on this blog about whether ABMs can truly capture/represent life – particularly in geographic terms.

I don’t completely agree with “GIS Funa” that ABMs should only be used as a means for “breaking down” complex phenomenon. While O’Sullivan appears to accept that ABMs often are used this way, he, himself, writes that while simple ABMs might be useful for exploring theories under particular assumptions, they could never be used to “establish the truth of those theories” (546).  He adds that their logic would never be more convincing than other “rhetorical device[s]” if this was the only manner they were used (546). He goes on to conclude that the challenge for modellers is to find more sophisticated ways in which to use ABMs (just as modelers working with other types of models have done).

While I believe ABMs might always be a bit soft in explaining potentially complex individual actions such as irrational behavior, subjective choices or other complex psychologies (as Eric Bonabeau suggests on p7287), they can approximate reality and account for geographic space. Furthermore, these approximations can be extremely useful in trying to better understand complex, emergent phenomenon, flows, or thresholds/state changes in a system. So, we must instead learn how to use ABMs for purposes where these strong suits can best be harnessed – while remaining aware of any limitations.


ABM’s and the Ballet

Sunday, January 29th, 2012

The Sidewalk Ballet speaks very critically of ABM’s: “Abstract ABMs disregard any detail of real world situations […]Things occurring in one space cannot be blindly applied to a different one without acknowledging the different factors which comprise and inhabit the space[…] then what is their use in GIScience?”

I will argue that very few ABM’s will claim to explain the whole issue being examined. I do not believe that the people responsible for the ant and the sugar model are attempting to explain income inequality. The metaphor there or the main purpose behind ABM’s in general is to simplify a certain problem to gain a better understanding.

If I were to, for example attempt at writing a piece of music, I would not write everything down at once—or if I were to decide deconstruct and learn how to play and conduct, let’s say Tchaikovsky’s Swan Lake ballet Op.20 written in 1875, I would not tackle the whole song at once. I would break down the components. First I might figure out what key the song was written in. I would then have a better understanding of what my options may be when choosing the notes throughout the song. From there I might teach myself different chords or scales that are required to play this song. Sure, if I blindly apply scales outside of the proper key into the song, my rendition might not sound good, but slowly, by gathering more knowledge of the problem I become closer to learning the piece as a whole. By understanding the key, time signature and tempo, I could then change the way I conduct the song. By changing one of these components, the mood of the song (outcome) can drastically be altered.

I’m not totally in favour of ABM’s, and I do understand some of the drawbacks, but I do think that they offer a way for us to break down complex phenomenon. As mentioned in class, the IPCC attempt, time after time, to model the impacts of GHG’s on climate change. People have been extremely critical of these models, but I believe that the point here is that models aid in the understanding of trends and patterns. Many miss the point and are very focused on proving these predictions (I will use this word gingerly) wrong.  By being able to control variables, much like key or time signature, researchers have the opportunity to forecast and understand possible outcomes with very little risk. By being able to change desired variables, we now have the ability to see how each one impacts the system. As basic as the variable sliding bars seen in class appear, they still represent an extremely powerful tool.

Great movie, great ballet.

Andrew “GIS” Funa


Bonabeau reading and acknowledging limitations of Agent Based Modeling

Saturday, January 28th, 2012

Bonabeau’s article goes to great lengths to illustrate the advantages of Agent Based Modeling (ABM). He provides a quick overview of the approach which consists of agents that independently make decisions towards their goals and a shared environment. The novelty of this approach is that it captures emergent behaviour and often counter-intuitive results by analyzing at the individual agent level (which is often highly heterogeneous). Bonabeau explains this modeling has been applied to many fields such as transportation, supermarket design, and stock markets.

Despite this broad applicability of ABM, it must be approached cautiously. I believe that there is a large gap between seeing a simulation of an emergent phenomenon and whether it can be validated as representative of reality. The accuracy of these simulations depends on the inputted parameters, which often must reflect difficult-to-quantify behaviours. An uncritical acceptance of ABM’s results can risk large sums of money, public trust, and lives.  

Furthermore, it is important to use ABM to its full potential. Users of this tool should not focus solely on running this model until they get a desired result. There is room for geographic analysis of unexpected emergent interactions to better explain conclusions. There is also a need for a deep understanding of the limited spatial analysis each agent is capable of, and how the agents’ perception of their spatial surroundings affects their behaviour.   

Bonabeau, Eric. “Agent-based Modeling: Methods and Techniques for Simulating Human Systems.” Proceedings of the National Academy of Sciences of the United States of America. 99.10 (2002): 7280-7287. Print.

– Madskiier_JWong

Agent-Based Modeling: Computation and Cost?

Thursday, January 26th, 2012

Agent-based modeling (ABM) can do ANYTHING — the basic claim being made by Eric Bonabeau in his article, Agent-based modeling: Methods and techniques for simulating human systems.  And indeed, it does appear that ABM is quite useful, particularly when examining heterogeneous populations, as we can see in “virtually every example in this article”, to quote the author himself.  While I still wonder about the validity of ABM in certain situations, and can’t help but feel unsure about the authors’ exuberant claims in his writing, there was one thing particularly that I found missing from this article: computation and cost.

While Bonabeau does devote one or two sentences at the very end of the article to the high level of computational power required for these types of models, he does not, in my opinion, adequately express not only how important this one factor may be, but also all the additional factors inherent with data-heavy models such as this.  For example, he makes no reference to the amount of data collection that must go into creating these models.  Even a basic GIS user understands that a superficial layer of data is not interesting, but anything more than that requires a lot of commitment to collecting data.  In this case, working with human systems, to me that implies surveying people about their behaviours, how they make decisions, and so on.  This means time and monetary commitment.  And this leads to my larger criticism: the most telling aspect was how the companies he referred to were primarily established, and I would assume, wealthy, companies or organizations who could afford to use ABMs to make better management decisions.  Despite this, nowhere does he discuss cost.  Surely this technology does not come cheap?  And if it does, wouldn’t that make it even more desirable, and worthwhile to include?

With this knowledge, the reader (and potential user) could make a more informed decision about if ABM is not only useful, but at all possible, for them.  In the end, an interesting overview of applications of ABM, but lacking in answers to a few important questions.

Bonabeau, Eric. “Agent-based Modeling: Methods and Techniques for Simulating Human Systems.” Proceedings of the National Academy of Sciences of the United States of America. 99.10 (2002): 7280-7287. Print.