Modelling & Simulation

Modelling for Change

This blog has originally been posted on

Can Agent-Based simulation models help us to improve services in complex WASH systems?

Practitioners in the water, sanitation and hygiene (WASH) sector use a variety of modelling tools to guide them in understanding and improving service delivery. Examples include financial modelling in spreadsheet models, graphic information system-based (GIS) modelling for geographic mapping of infrastructure and conceptual flow modelling in a sanitation system. These tools are powerful in their respective area of interest. However, in this blog, I advocate for the use of a complementary modelling tool that will help us to understand and analyse complex social interactions in WASH: an Agent-Based Modelling (ABM) tool. ABM can help practitioners to:

  1. diagnose the system;
  2. explore the effects of policy interventions; and
  3. discuss with partners and clients how the theory of complex systems affects them.

Policy Interventions in WASH

In a previous IRC blog, I described WASH as a Complex Adaptive System (CAS). This perspective taught us that a system concerned with the delivery of water or sanitation services is complex. Complexity theory investigates how relations between parts in a system result in a collective, observable behaviour. We can translate this to interactions and relationships between donor organisations, governments, service providers, technical infrastructure and water resources – the parts of the system – that result in a certain level of service delivery – the collective, observable behaviour.

How stakeholders and people involved in service delivery react to each other and to a policy intervention is very difficult to predict. (Policy) interventions can result in different outcomes due to unforeseen and unexpected reactions and interactions among the people, organisations and governments involved. This creates a major challenge for improving service levels.

In an ideal situation we are able to test any intervention we plan beforehand, turn back the time if the intervention does not go according to plan, and try a new intervention. Time and time again, until we get it right. Unfortunately, this is not possible. The best we can do is make a well-educated guess of the effect of a policy. We can support this guess with calculations, by consulting stakeholders and by piloting the policy in an isolated environment. ABM is a method that investigates and anticipates the interactions between people and organisations. Furthermore, ABM can be used to test an intervention, turn back time, and test another intervention. The premise of ABM can be summarised as follows:

1. ABM is a tool for diagnosis

In an ABM a modeller determines relations between agents. These agents form the key entities in an ABM and can be anything: a person, a hand pump, an organisation or a country. The agents are given a set of decision rules. Based on the defined relations and decision rules the agents interact with each other, resulting in some form of observable behaviour.

The core of an ABM is nothing more than lines of code.

Collaborating for good policies in the water sector

This blog has originally been posted on

Exploring the effects of different ways in which a policy can evolve through collaboration in a social system.

How can learning in the Ugandan rural water supply sector be conceptualised and modelled? As I am halfway through writing my thesis I’d like to discuss why ‘learning’ in the question above actually does not refer to learning as it is commonly understood and what possible answers the field of memetics may offer to the question.

Uncertainty in a system

When we analyse a system we have information about the current state of the system. The future state of a system, however, is difficult to predict. We do not know what events will occur and what their impact will be. When we make predictions over longer time horizons, we are even less certain. Most people do not like uncertainty. We can formulate two strategies: 1) Predict the future state really well and 2) take uncertainty as ‘a given’ and adapt the structures in the system.

In adaptive management, the cycles of policymaking are shorter and policies are used to test hypotheses about the behaviour of the system. Good policies are adapted and scaled up, others are rejected, and new ones are tested. Organisations in the sector that are able to ‘learn’ better contribute to the adaptiveness of the sector. If an organisation is able to quickly learn the effects of their actions, the policy cycles in the system are assumed to get shorter.

Learning can take place at two levels:

  1. organisations or individuals that perform an action can learn the effect of their action;
  2. an organisation can learn from another organisation what actions to take.