Making smart decisions. Together.
The whole is greater than the sum of its parts.
Collaboration among stakeholders across disciplines, departments, units or domains is key to solve internal and external problems in a complex world.
The collaborative modelling approach is an integrated solution that helps groups of stakeholders tackle major and often complex and sensitive challenges and reach consensus during their decision making process in an agile and smooth way.
We achieve this by combining collective intelligence and computation based modelling techniques.
Our collaborative modelling approach is supported by digital tools; which makes it easier for stakeholders to convey relevant conclusions in a short period, to track previous results and foster outcome adoption in a simple way and at every step.
Together with the customer we define the scope and the scale of the project and identify the right group of stakeholders for the collective intelligence session.
Based on existing data and the literature, CORESO writes a white paper that will serve as a starting point for the collective intelligence sessions
Together with the group of stakeholders, we apply collective intelligence techniques to gain first-hand insights on the topic and identify open questions.
CORESO co-develops a model that will allow stakeholders to analyse possible consequences of their decisions - even before making them!
The model is used in the decision-making process so that decisions rely on a more solid basis and have clearer insights on possible consequences.
CORESO assists stakeholders both in their implementation phase and in identifying new potential questions to answer.
Collective intelligence is the combined intelligence of the members of a group of people, which is typically larger than the sum of the individual knowledge. Our toolbox of techniques is designed to capture individual insights and collect them to accurately reflect the group’s knowledge. The aim is that the group reaches consensus smoothly despite competing interests, knowledge and objectives.
The idea behind is to value each piece of knowledge and expertise from different stakeholders over an issue at hand. Most problems are complex and involve a diversity of stakeholders; all having different views of the same issue and keen to share their perspectives to be considered for the decision-making.
Let’s assume a company would like to harmonise the adoption of its corporate values across different departments. Such a change can be scary and could generate friction between employees and departments. Collective intelligence is a technique which allows participants to tackle such challenges as an organisation-wide process where all hierarchical levels and departments can contribute and are being heard. The aim is to reflect different interests and values and combine them into a balanced solution; which is more easily and widely adopted by the employees.
Public policy design
Designing sound public policies is a difficult task because decision-makers need not only take into account objective arguments on what would be best to do, but they also have to consider the acceptance of a solution among the concerned population to increase the adoption. Collective intelligence techniques can help policymakers bring stakeholders together at an early stage of the policy design and find a balanced and acceptable policy proposal. Our semi-anonymous and anonymous techniques help groups of stakeholders find solutions even for sensitive topics with large and competing groups of interest.
CORESO can facilitate a variety of methods - both traditionally and digitally assisted - including facilitation, focus groups, brainstorming workshops, online surveys and more.
Our innovative computational models can support you and your group of stakeholders throughout the analysis of complex issues and introduce an accurate evaluation of what would happen under different scenarios.
Today most problems are complex, both at the public policy level or within firms, and involve a variety of actors. These actors do not always have the same vision nor the same interests. Interactions among them are frequent and should be mapped in a system to make them understandable. Simple techniques and linear models are rarely suitable to analyse such interactions.
To understand complex systems, our modelling specialists analyse and translate insights into a computational model, so that decision-makers can run what-if scenarios before moving on to implement any new policy.
WHERE CAN THIS BE APPLIED?
Advanced statistical models can help to better understand causal relationships and distinguish real causal effects from less informative statistical associations. Understanding such differences is crucial for the decision-making process. Our modellers are trained to get all out of your data while avoiding fast conclusions on false claims.
Micro-simulation models are often used to describe the public finance system. They allow us to analyse at the individual level how tax and social security reforms would affect the population. Such fine-tuned models go beyond average effects and allow us to identify the gains and losses for the whole population.
At CORESO we use modelling techniques such as agent-based modelling, data science, Geographic Information Systems (GIS) and more.
The strength of collaborative modelling lies in the combination of the virtues from collective intelligence and computational modelling techniques.
This integration of both human and digital components aims at understanding first-hand people’s perspectives as well as the complexity and scale of a system associated with a given problem. Always keeping the human factor at its core; it allows small and large groups of stakeholders to jointly build with our modellers a better and richer picture of the issue at hand. Our work is to translate the scale and level of complexity into a more understandable piece that stakeholders can use to analyse their problem in a more organised, clear and targeted fashion.
These models will then help stakeholders to see the consequences of their decisions before making them by clarifying different perspectives, challenging themselves on the effect that elements can have on one another and evaluating outcome adoption.
This approach is supported by methodologies such as Design Thinking, which is an innovative user-centric approach for experimentation with prototypes and product or service testing.
We often hear about unequal salaries, but this is only an indicator or outcome of a complex human resource system within a company. The collaborative modelling approach can help us to virtually recreate this system and then analyse different policy measures. The collective intelligence of members of the company will help modellers to bring the model very close to reality. Then the model will help the group to identify the best policy measures and also estimate the time frame in which positive outcomes can be expected.
Organising the multimodal traffic flow in cities is very complex because a multitude of different participants are present and the space is limited. Policy changes in the organisation of traffic regularly create protests of some interest groups. Using our collaborative modelling approach allows not only to simulate the possible outcomes of policy measures but also to take advantage of specific knowledge of inhabitants and to find solutions that are acceptable for the different groups of interest.
Health Care System
In most countries, the health system is very large and ever-growing costs are at the public debate. Finding sound public policies is difficult because of the complexity of the healthcare system and the different interests present. Simple analyses of parts of the system do not allow decision-makers to understand the system-wide consequences of their decisions. Our collaborative modelling approach is well suited for such an exercise because the different groups of interest can provide inputs to the model and help make the model more accurate. The model itself can then inform policy-makers and stakeholders about the different system-wide effects of policy measures. This can help to make the policy process more objective and less subject to the power of interest groups.
You can see from the image below that the components supporting this approach allow us to model systems that can often involve high levels of sensitiveness and complexity. Relying on this approach can turn complexity into something understandable and extract valuable perspectives from sensitive topics. As a result, a rich and clear picture can be delivered to participant stakeholders to jointly analyse and decide upon. Always keeping the human factor at the core.