19 Sep 2011

CfP: Workshop on Modelling Policy-making (MPM 2011)

Vienna, Dec 12 or 13th 2011.

In conjunction with
The 24th International Conference on Legal Knowledge and Information Systems (JURIX 2011)

Submission Focus:
The workshop invites submissions of original research about the application of ICT to the early phases of the policy cycle, namely those before the legislation is fixed by the legislators: agenda setting, policy analysis, and lawmaking. The research should seek to address the gap noted above. The workshop focusses particularly on using and integrating a range of subcomponents – information extraction, processing, representation, modelling, simulation, reasoning, and argument – to provide policy making tools to the public and public administrators.

Deadline: 24th October

http://wyner.info/LanguageLogicLawSoftware/index.php/2011/09/18/workshop-on-modelling-policy-making/

15 Sep 2011

My ECCS 2011 Poster: Using Data Sets to Simulate Evolution within Complex Environments (A0 Poster)

Poster:
http://www.slideshare.net/BruceEdmonds/using-data-sets-to-simulate-evolution-within-complex-environments-poster

Abstract:

An agent-based model of evolution and adaption is presented with complex genomes, sexual and asexual reproduction. However, unlike other models, this has a purposefully complex environment. Individuals are spread over a 2D space upon which is projected a complex data set derived from observations of the real world. Energy extraction for the purposes of life and reproduction is achieved by the match between the genome and the data in its locality. In this model different genomes develop to exploit different parts of the space, with some genomes acquiring a greater generality than others. This model can be used to explore the trade-offs inherent between diversity, specialization and inter-breeding among individuals.

My talk at CoDyn@ECCS 2011: Modelling Belief Change in a Population Using Explanatory Coherence

Slides:
http://www.slideshare.net/BruceEdmonds/modelling-belief-change-in-a-population-using-explanatory-coherence

Abstract:
A simulation model is presented that represents belief change, based on Thagard’s theory of explanatory coherence, within a population of agents who are connected by a social network. In this model there are a fixed number of represented beliefs, each of which are either held or not by each agent. These beliefs are to different extents coherent with each other – this is modelled using a coherence function from possible sets of core beliefs to [-1,1]. The social influence is achieved through gaining of a belief from another agent across a social link. Beliefs can be lost by being dropped from an agent’s store. Both of these processes happen with a probability related to the change in coherence that would result in an agent’s belief store. A resulting measured “opinion” can be retrieved in a number of ways, here as a weighted sum of a pattern of the core beliefs – opinion is thus an outcome and not directly processed by agents. Results suggest that a reasonable rate of copy and drop processes and a well connected network are required to achieve consensus, but given that, the approach is effective at producing consensuses for many compatibility functions. However, there are some belief structures where this is difficult.

My talk at Policy Modelling@ECCS 2011: Four different views of a policy model : an analysis and some suggestions

Slides:
http://www.slideshare.net/BruceEdmonds/four-different-views-of-a-policy-model-an-analysis-and-some-suggestions

Abstract:

A policy model has (at least) four different interpretations: (a) intention: the intention/interpretation of the simulation designer/programmer, (b) validation: the meaning established by the validation of the model in terms of the mapping(s) to sets of evidence, (c) use: the meaning established as a result of the use of a model in a policy making/advice context and (d) interpretation: the narrative interpretation of the policy maker/advisor when justifying decisions made where this refers to a policy model. 

These four different interpretations are loosely connected via social processes.  The relation between intention and validation is relatively well discussed in the context of “scientific” model specification and development.  The relation between use and interpretation has been discussed in a number of specific contexts.  However when and how a relationship between the scientific world of intention/validation and the policy world of use/interpretation are established in practice is an area with little active research. 

Both personal experience and philosophical considerations suggest that these two worlds are very different in terms of both purpose and method.  However this does not mean that there cannot be any well-founded connection between them.  The key question is understanding the social processes of how this can happen, what are the conditions that facilitate it happening and what is the nature of the relationship between the four views when it does happen.

Interestingly these issues have been faced and extensively discussed in the field of Artificial Intelligence, which has confronted the distinction between meaning of internal models (loosely, the beliefs of an agent about its environment) in these four ways.  The field of AI has not come up with a final solution to these problems, and is itself divided into those that inhabit separate approaches that adopt a subset of these approaches to model meaning.  However it is suggestive of some ways forward, namely:

•    a recognition of the problem that there are these different ways of attributing meaning to a policy model (and hence avoid some common errors derived from conflating these four views);
•    symbol grounding in the sense of learning meanings through repeated use and adjustment (either in response to validation or interpretation views or both);
•    and the observation of scientific-policy interaction as it actually occurs (e.g. an ethnographic study of scientist/policy advisor interaction). 

Some developments in the area of participatory policy modelling can be seen as forays into this arena, albeit without structured assessment.

My talk at NESS@ECCS 2011: Mundane Rationality as a basis for modelling and understanding behaviour within specific contexts

Slides:
http://www.slideshare.net/BruceEdmonds/mundane-rationality-as-a-basis-for-modelling-and-understanding-behaviour-within-specific-contexts-9251997

Abstract:
The paper starts out by pointing out the context-dependency of human cognition and behaviour, pointing out that (a) human behaviour can change sharply across contexts but also that (b) behaviour within a given context can sometimes be described in relatively simple terms .  It thus argues against a grand theory of rationality that seeks to explain and/or generate human behaviour across of contexts.  Rather it suggests an alternative approach whereby  "mundane" accounts of rationality are used which are specific to a limited number of contexts.  Such an approach has its particular difficulties, but allows the integration of narrative accounts of possible behaviours using a variety of social mechanisms at the micro level with comparisons with aggregate macro data.  It is noted that in the resulting simulations that equilibria are simply not relevant within plausible timescales.

My talk at ECCS2011: Using a Data-Integration Model to Stage Abstraction in Voter Turnout

Slides:
http://www.slideshare.net/BruceEdmonds/using-a-dataintegration-model-to-stage-abstraction-in-voter-turnout

Abstract:

A simulation model is presented that represents belief change, based on Thagard’s theory of explanatory coherence, within a population of agents who are connected by a social network. In this model there are a fixed number of represented beliefs, each of which are either held or not by each agent. These beliefs are to different extents coherent with each other – this is modelled using a coherence function from possible sets of core beliefs to [-1,1]. The social influence is achieved through gaining of a belief from another agent across a social link. Beliefs can be lost by being dropped from an agent’s store. Both of these processes happen with a probability related to the change in coherence that would result in an agent’s belief store. A resulting measured “opinion” can be retrieved in a number of ways, here as a weighted sum of a pattern of the core beliefs – opinion is thus an outcome and not directly processed by agents. Results suggest that a reasonable rate of copy and drop processes and a well connected network are required to achieve consensus, but given that, the approach is effective at producing consensuses for many compatibility functions. However, there are some belief structures where this is difficult.