Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability

The integrated - environmental, economic and social - analysis of climate change calls for a paradigm shift as it is fundamentally a problem of complex, bottom-up and multi-agent human behaviour. There is a growing awareness that global environmental change dynamics and the related socio-economic implications involve a degree of complexity that requires an innovative modelling of combined social and ecological systems. Climate change policy can no longer be addressed separately from a broader context of adaptation and sustainability strategies. A vast body of literature on agent-based modelling (ABM) shows its potential to couple social and environmental models, to incorporate the influence of micro-level decision making in the system dynamics and to study the emergence of collective responses to policies. However, there are few publications which concretely apply this methodology to the study of climate change related issues. The analysis of the state of the art reported in this paper supports the idea that today ABM is an appropriate methodology for the bottom-up exploration of climate policies, especially because it can take into account adaptive behaviour and heterogeneity of the system's components.Review, Agent-Based Modelling, Socio-Ecosystems, Climate Change, Adaptation, Complexity.

A model for characterising the collective dynamic behaviour of evolutionary algorithms

Exploration and exploitation are considered essential notions in evolutionary algorithms. However, a precise interpretation of what constitutes exploration or exploitation is clearly lacking and so are specific measures for characterising such notions. In this paper, we start addressing this issue by presenting new measures that can be used as indicators of the exploitation behaviour of an algorithm. These work by characterising the extent to which available information guides the search. More precisely, they quantify the dependency of a population's activity on the observed fitness values and genetic material, utilising an empirical model that uses a coarse-grained representation of population dynamics and records information about it. The model uses the k-means clustering algorithm to identify the population's "basins of activity". The exploitation behaviour is then captured by an entropy-based measure based on the model that quantifies the strength of the association between a population's activity distribution and the observed fitness landscape information. In experiments, we analysed the effects of the search operators and their parameter settings on the collective dynamic behaviour of populations. We also analysed the effect of using different problems on algorithm behaviours.We define a behavioural landscape for each problem to identify the appropriate behaviour to achieve good results and point out possible applications for the proposed model

A Model for Analysing the Collective Dynamic Behaviour and Characterising the Exploitation of Population-Based Algorithms

Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify features associated with it. The proposed model studies the topological distribution of an algorithm's activity from both a genotypic and a phenotypic perspective, and represents population dynamics using multiple levels of abstraction. The model can have different instantiations. Here it has been implemented using a modified version of self-organising maps. These are used to represent and track the population motion in the fitness landscape as the algorithm operates on solving a problem. Based on this model, we developed a set of features that characterise the population's collective dynamic behaviour. By analysing them and revealing their dependency on fitness distributions, we were then able to define an indicator of the exploitation behaviour of an algorithm. This is an entropy-based measure that assesses the dependency on fitness distributions of different features of population dynamics. To test the proposed measures, evolutionary algorithms with different crossover operators, selection pressure levels and population handling techniques have been examined, which lead populations to exhibit a wide range of exploitation-exploration behaviours. </jats:p

A Conceptual Framework to Model Long-Run Qualitative Change in the Energy System

This paper deals with a conceptual framework allowing the analysis of long-run qualitative change in the energy system. The energy sector seems to be particularly appropriate for the analysis of qualitative change due to the following reasons: The energy sector is relevant for the development of the whole economy. When looking on the development of primary energy resources it becomes obvious that different energy sources are of different importance over time and that new energy sources enter the scene from time to time. E.g. the importance of wood is decreasing over last 200 years, whereas coal has reached its peak around the turn of the last century, natural gas entered the scene not before that time. Nuclear energy technologies emerge in the energy supply only after 1960s. Furthermore, compared to other sectors qualitative change in the energy sector proceeds in relative long time periods. Accordingly, different mechanisms and effects are comparatively easier to separate as not too many overlapping developments are considered to appear simultaneously, which makes the discrimination of causes and effects more difficult. Related to this, it is not invention that plays a particular important role but it is both innovation as the first commercial application and diffusion as the spreading out of the new technologies. This means that in the analysis strong technological uncertainty does play a minor role, most often the relevant technologies do already exist as blue-prints and the transformation process basically deals with the application and improvement of these technologies.energy; qualitative change; agend based models

Andmepioneeride suurandmetega seotud kujutluspildid: muutunud andmesuhted ja agentsust puudutavad väljakutsed

Väitekirja elektrooniline versioon ei sisalda publikatsiooneSuurandmeid kujutatakse sageli kui midagi müstilist, mis toob kaasa efektiivsema, õiglasema, ning parema hüvede jaotamise ning võimaldab meil paremini mõista ja uurida erinevad ühiskondlikke protsesse. Suurandmetel põhinevate analüüside taga on alati aga (andme) eksperdid, kellel on suur roll nii tänase digiühiskonna kujundamisel kui ka uurimisel. See, kuidas nemad suurandmeid mõistavad ning mil moel uusi andmete kogumist ning analüüsimist puudutavaid võimalusi kasutavad mõjutab aga ka seda, mida nii üksikisikud kui ka teised institutsioonide andmetega seoses teha saavad. Doktoritöö „Andmepioneeride suurandmeid puudutavad kujutluspildid: muutunud andmesuhted ja agentsust puudutavad väljakutsed“ eesmärk on analüüsida suurandmetega seotud kujutelmi – selgitada välja, milliseid võimalusi ning väljakutseid andmeeksperdid kui andmepioneerid näevad seoses suurandmete analüüsimise ja andmetehnoloogiate kasutamisega ning kuivõrd on need seotud ühiskonnas ja meedias domineerivate kujutelmadega. Selle eesmärgi täitmiseks tugineb käesolev töö intervjuudele Eesti andmeekspertidega, süstemaatilisele sotsiaalmeedia andmeuuringuid puudutaval ülevaatel kui ka representatiivsetel populatsiooni küsitluse andmetele. Doktoritöö tulemustest nähtub, et suurandmeid kujutatakse andmeekspertide poolt enamjaolt kui väärtuslikku ressurssi, mis pakub võimalusi muuta otsustusprotsesse kiiremaks, aga ka paremini mõista sotsiaalseid protsesse ja uurida inimeste käitumist. Ekspertide kujutelmades on andmed ka üha enam vaadeldavad kui kaup või kapital, mis on oluliseks konkurentsieeliseks nii era- kui ka avaliku sektori organisatsioonide jaoks. Ka toob töö välja mitmeid takistusi nii suurandmete kasutamisel kui ka uurimisel nagu andmetele juurdepääsu probleemid, ebapiisavad oskused ja teadmised suurandmete analüüside osas, andmete jagamiseks vajalike standardite puudumine, andmesubjekti õiguste kaitseks seotud seaduslikud piirangud, aga ka muutunud andmesuhted.The use of big data is often portrayed both in the media and by practitioners as an opportunity, an increase in efficiency or an opportunity to better control certain processes in society. Data, especially big data, is often referred to as something mystical in these contexts. At the same time, there are always (data) experts behind the analysis based on big data, who play a major role in shaping and researching today's digital society. The aim of the dissertation "Big data imaginaries of data pioneers: changed data relations and challenges to agency" The aim of my thesis was to analyze how are the dominant big data imaginaries actualized and elaborated amongst data pioneers, how has this affected the scholarly practices and challenged the individual and collective agency. To achieve this goal, I interviewed the Estonian data experts, collected data by using a systematic review method as well as representative population survey data. The results of the dissertation show that big data is mostly seen by data experts as a valuable resource that provides tools to improve governance through better and more efficient decision-making. Moreover, it is seen to provide an opportunity to better understand social processes and study human behavior. Data in the imagination of experts is also increasingly seen as commodity or capital, which is a significant competitive advantage for both private and public sector organizations. This thesis also brings out several barriers in relation to using and researching big data like the access to data, insufficient skills and knowledge to gather or analyze (big) data, lack of unified standards needed for sharing data between different parties, legal restrictions usually posed to protect the data subjects’ rights, technological affordances as well as changed data relation

Using Qualitative Evidence to Enhance an Agent-Based Modelling System for Studying Land Use Change

This paper describes and evaluates a process of using qualitative field research data to extend the pre-existing FEARLUS agent-based modelling system through enriching its ontological capabilities, but without a deep level of involvement of the stakeholders in designing the model itself. Use of qualitative research in agent-based models typically involves protracted and expensive interaction with stakeholders; consequently gathering the valuable insights that qualitative methods could provide is not always feasible. At the same time, many researchers advocate building completely new models for each scenario to be studied, violating one of the supposed advantages of the object-oriented programming languages in which many such systems are built: that of code reuse. The process described here uses coded interviews to identify themes suggesting changes to an existing model, the assumptions behind which are then checked with respondents. We find this increases the confidence with which the extended model can be applied to the case study, with a relatively small commitment required on the part of respondents.Agent-Based Modelling, Land Use/Cover Change, Qualitative Research, Interdisciplinary Research