List of deliverables


WP1: Project Regulation

WP2: Modelling Climate Related Energies

WP3: Realising Mitigation Strategies

WP4: Socio-Economic and Land Use Dynamics in the Stockholm-Mälar Region

WP5: Energy, Economy and Society

WP6: Model Integration

WP7: Dissemination, Integration and Exploitation

This page contains links to COMPLEX scientific reports, policy briefs and discussion documents.

You can find links to COMPLEX publications our publications page.

Final Scientific Report, Volume 1: The Quest for a Model-Stakeholder Fusion

Final Scientific Report, Volume 2: Non-linearities and System-Flips

Final Scientific Report, Volume 3a: Establishing Policy-Relevance: Human-Environment Interaction

Final Scientific Report, Volume 3b: Establishing Policy-Relevance: Developing and Evaluating Policy Options

Final Scientific Report, Volume 4: What we did and why it matters


The Behavioural Ecology of Project-Based Science



The model integration framework is developed to link models which are developed using different programming language and which could be located on different location. It is developed using web service based approach - models are wrapped with web services and the integration framework handles the synchronization, semantic mediation, and data conversion functionalities.

The integration framework organizes its content in different menus. Here we present brief description of the main menus of the system.

1.Wrapped Models Menu

This menu consists of models wrapped with web services. Models developed using GAMS, NetLogo, Java, and C++ are presented as web service. Users can access this web service based models as stand-alone systems. A user can also provide inputs and run simulations.

2. Integration Menu

The integration menu consists of interfaces that link the web service based models.

  • Sensitivity of integration submenu: it links stand-alone predator and prey models so as to experiment the effect of integration methods and time steps used in linking models. Further detail on the experiments we performed can be found on our paper Exploring temporal and functional synchronization in integrating models: A sensitivity analysis.
  • EXIOMOD-GCAM submenu: it links EXIOMOD model with GCAM. The user can set inputs, run simulation, display and download output.
  • EXIOMOD-AbEMM submenu: it links EXIOMOD model with the AbEMM model.
  • Airports Vs Weather submenu: this interface links the Airports web service with Global Weather web service. The main objective of this interface is to experiment on our semantic mediation module. It demonstrates semantic mediation of text-based input-output data using word overlapping algorithm and also using lexical database.

3. Runtime Applications Menu

This menu consists of two interfaces that focus on runtime access and integration of models.

  • Runtime Access to models interface which provides a user interface to access web service based models during runtime. The user is expected to provide the URL of the web service, then the system will explore the existing methods together with the corresponding input and output variables.
  • Runtime integration interface is designed to link two web service based models during run time. If the output of the first model can be used as input for the second model then the user can define the data exchange pattern and can link the two models. However, if data mediation is required the user can develop the data mediation module as web service and can use the data mediation module in linking the models.

4. Semantics Menu

This menu consists of forms that demonstrate how freely available ontology can be used in semantic mediation, and different semantic matching algorithms in semantic mediation.

  • Units Ontology: we used this interface to demonstrate how we can minimize hard-coding in semantic mediation by using an existing ontology. The QUDT Ontology is freely available ontology developed to describe Quantities, Units, Dimensions, and Data Types. The units ontology is used to perform automatic unit conversion computations in integrating models.
  • Semantic matching interfaces are used to experiment the effect of lexical databases in semantic mediation process of integration. In these interfaces we used in-house developed semantic matching algorithm and freely available lexicon database - WordNet.
  • Matching CSDMS metadata: in this interface we demonstrated how semantic mediation can be applied in searching for models which could provide data to our models. The semantic mediation techniques are applied on CSDMS Standardized Model Metadata.

 The model integration framework can be accessed using the following link

Several key mental processes are involved in decision making (DM). Our objective is to contribute to an understanding of the relation between individual decisions of citizens and the decisions to be taken by policy makers. Our computational model includes effects of personal factors, behavior and environmental factors, based on neural structures, dynamics and functions.

Our main approach is using neuro-computational methods to tackle personal factors and behavior of individuals in DM. At the individual level, we have used Kahneman’s ideas of “thinking fast and slow”, as a paradigm when modeling the interaction of emotion and cognition in DM. The amygdala, orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) were considered as the major neural structures underlying decision making.  The interaction of the first two structures plays a remarkable role in the emotion perception and the emotional response, while the rational decisions are evolved at the latter structure. The resultant of emotional and rational selections infers the final decision making strategy.

The neural networks of the three modeled brain structures control DM with respect to some individual and environmental parameters. Stored emotional/rational experiences and individual principles are the basis of the attitude formation. Internal states such as fatigue, anger, happiness, etc. are considered as influential parameters on emotional decision.  The self-control power, the contribution of emotion and rationality in making decisions, determines the result of the competition between these two systems. Considering the human being as an isolated agent, internal stimuli and environmental conditions (e.g. availability of options) are the only parameters affecting the behaviour.

The model is intended to represent an adaptive DM under varying internal and external contexts. Experimental results indicate the involvement of different neural structures in the DM process, but to simplify the model, we focus our attention on three of the most crucial neural structures discussed above (amygdala, OFC and LPFC). The model takes into account the perception-action cyclic process, which involves both emotional and cognitive aspects, and is modeled as an interaction between System 1 (amygdala and OFC) and System 2 (LPFC).

The structures and dynamics of the three brain areas are modeled with attractor neural networks and mesoscopic neurodynamics (see below). Oscillatory rhythms encode information related to perception, cognition and emotional associations in our model. The oscillatory network behavior is a result of the interaction between excitatory and inhibitory neural populations (network nodes), as described below, and in more detail in Liljenström (1991, 2010). The network activity can be envisioned as local field potentials (LFP) or electroencephalogram (EEG) readouts (Liljenström, 2010, 2012).

The model used is based on a previously developed cortical neural network model (Liljenström, 1991), which here has been extended and modified to mimic the structures of amygdala, OFC and LPFC, respectively. 

The upper and lower layers each consists of 25 inhibitory nodes and the middle layer of 100 excitatory nodes. The external inputs stimulate (a subset of) the excitatory nodes. The stimulation of excitatory nodes initiates the activity of the system, and in turn excites inhibitory nodes, resulting in an oscillatory excitation-inhibition balance.

 For more information contact: 

  • Hans Liljenström, email: This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Azadeh Hassannejad Nazir, email: This email address is being protected from spambots. You need JavaScript enabled to view it.