A glossary of terms used on this site...
Actors is a term used to describe the entities that operate within a system. They are most commonly people, households, firms, or other organisations.
Agent-based models of social and policy processes are computer models designed to simulate the behaviour of people, households, firms, NGOs, and/or government (i.e., the 'agents'). By building a computer simulation (writing computer code to represent behaviours and interactions) we are able to create a simulated world which we can experiment on. Experiments might include changing policy scenarios, or changing assumptions in the model. Writing computer code forces us to be rigorous and specific in detailing how we believe agents operate, but it is also flexible enough to allow us to represent many different types of agents. Models of this type can be useful for developing our understanding of a process, exploring scenarios, or it can be the process of building itself that is useful, when we engage stakeholders in the design process.
A causal model is an abstract model that describes the causal mechanisms of a system. The model must express more than correlation because correlation does not imply causation.
The concept of controllability refers to the ability of any stakeholder, or group of stakeholders, to influence a factor in a system. Factors will have a high controllability if stakeholders can influence it as they wish without creating unintended or undesirable effects.
Complex vs Complicated
The distinction between these two terms in everyday English is negligible, but in the context of Complexity Science they tend to be used in the following way. Complex systems are made up of many parts, with many interactions. Crucially, this makes them difficult to understand and predict (e.g., the economy, an ant colony). Complicated systems may have many parts and interactions, but these can be tracked and understood, and the behaviour of the system can be predicted (e.g., a stereo, or car engine).
Complex Adaptive Systems
Complex Adaptive Systems (CAS) is the name given to systems that exhibit certain common behaviours such as tipping points, self-organisation, emergence, and adaptation. Have a read of our key concepts section, and read below, for more on these concepts.
Components (of a system)
The 'things' in a system are often referred to as the components of that system. They might be people, or firms, or power plants, or institutions like government departments. The term is often used as a catch-all to refer to the things that make up a system.
The context of a system usually refers to the environment, or situation in which it is being or operating. For example, an economy will operate within a political and legal context, i.e., the laws and policies which govern parts of its operation. The context may also refer to a physical or geographical environment. Again, an economy operates within a physical environment, which may affect its primary production (e.g., oil production, mineral extraction, water supply), and the ability of people and firms to trade and travel, for example.
Data is simply information, however it can come in many forms. The most intuitive examples are numbers or words. A spreadsheet is a common structure for storing information. For example, rows on a spreadsheet may represent individual respondents to a survey (e.g., Person 1, Person 2 etc), and the columns, their various answers to questions (e.g., yes, no, maybe).
Discrete refers to the concept of individual separate and distinct measures of something, as opposed to 'continuous' scales. For example, when counting children in a class, we use a discrete measure of individual human beings. It does not make sense to say there are 20.46 children in a specific class, as it is impossible to have 0.46 of a person. However, if we were to measure the height of children, this would be a continuous measure, as it does make sense to say a child is 135.8cm tall, or the average height was 130.3cm.
When talking about complexity, 'dynamics' can be used to a few different concepts. It may be used to refer to the interactions and actions in a system, for example, the dynamics of people trading. It may alternatively refer to the changes in some variable or measure through time, for example, the dynamics of a stock price. Often it is the passing of time, or the propensity for a system to change, that is central when we discuss dynamics.
An emergent phenomena refers to the appearance of larger patterns or regularities that arise from the interaction of smaller components of a system. Importantly, emergent phenomena are difficult, or impossible, to describe using only the smaller components of a system, or their simple aggregation. Read more in Concepts.
Empirical is an adjective that means derived from observation or experience rather than theory. Observations can come from surveys or interviews, scientific instruments, or direct personal experience.
In the context of complexity science and systems, environment is often used to refer to a more abstract 'background' to processes or events, than the common usage of the word refers to. The environment could be physical space such as the land or geography of an area, or it could be the policy (i.e., what policies a process is influenced by), social, or cultural background to our topic of focus. See also Context.
Factors are anything which may have an influence on your system. They are typically variables (something that can increase or decrease) within your system. They can be from any domain, technical, social, economic, political, ecological etc. They can be quantifiable, e.g. prices, or qualitative such as social attitudes.
Feedback loops occur when an output or result of a process has an effect on inputs or factors in that process, meaning the influence 'loops around'. Feedback can be positive or negative. Positive is when the output increases the 'level' of input, which in turn increases the level of output. For example, in a crowd, people becoming panicked is likely to make others walk or run faster, which is in turn likely to make more people panicked. A negative feedback occurs when the output creates a situation in which it influences the system to reduce the level of the output in the future. For example, our bodies sweat (outcome) if we are hot (input), which cools us down so we produce less sweat, or we shiver (outcome) if we are cold (input) to warm us up, thus reducing shivering.
Here, formalising refers to our efforts to define clearly how we believe a system or process works. This may involve representing the process or structure in mathematical form, or writing a computer program that simulates the process. It could also simply be describing the process in specific language, or drawing a flow diagram of the process.
A free rider is someone who uses some service or resource but does not pay for it. The 'free rider problem' is a well-known subject in economics. An example might be people who do not pay taxes. They still benefit from many of the services provided by the state, but did not contribute as others do.
Heterogeneity here refers to the difference of things, typically people, in a system. We may say people are heterogeneous when they have important differences, such that we can't lump them together or treat them the same in our models or indeed with our decisions. The opposite is homogeneous, when things are the same.
To immerge means to disappear or sink back into something. In the context of complexity science it is used to describe effects or factors that disappear into the noise of a system are thus difficult or impossible to pull out or detect.
Industrial ecosystems is the term used when we look at industrial systems through the lens of industrial ecology, which studies the material and energy flows through industrial systems. This approach emphasises the (potential) similarities between industrial and ecological systems.
Interventions refers to changes intentionally made by us, in an attempt to alter the behaviour of a system. This could be in the form of an incentive for people to make a certain decision, or in changes to the structure of how decisions are carried out. For example we might make bus tickets cheaper to encourage their use, or simply make it more convenient to pay for bus tickets to encourage their use.
Iteration is the process of repeating an action, often with the aim of improving on the outcome of that action. For example, we may iteratively set the price of a product so that it can react to changes in sales.
Levers / Leverage Points
Levers or leverage points are points in a system at which we may wish to make an intervention. These will likely have been identified as points at which an intervention may be effective. The lever may be a decision or process, such as the decision to buy a bus ticket, or it may be an ongoing adaptive process, such as commuting. If we want to encourage more bus use, instead of car use, we may use the specific decision of buying bus tickets as a leverage point (i.e., reduce price or make more convenient), or we may try to change peoples' attitudes towards commuting more generally, thus using that as our leverage point.
Lower or Micro level / Higher or Macro level
When we refer to lower/micro or higher/macro levels in a system we are referring to either the individual components of that system (lower), or the overall behaviour of a system (higher). For example, for a city, the lower level might be the homes, people and businesses that make up a city, and the higher level will be patterns of land use, aggregate number of residents, or total turnover of businesses, for example.
By modelling, we refer to large set of techniques (e.g., simulation models, statistical models, flow diagrams) that are intended to simplify a system, problem or process, in order to help us understand its dynamics and structure better. George Box famously said that 'all models are wrong, but some are useful'. When we engage in modelling, we are trying to harness this usefulness to help improve out understanding or decision making, but we must remember our models are simplifications, and are often wrong in some sense.
Networks / Network Graph / Nodes / Edges
Networks are a powerful tool for thinking about how components of a system are connected. You may be familiar with network diagrams - called 'graphs' - with dots joined by lines. These network diagrams are made up of 'nodes' and 'edges'. The nodes represent the entities, or components, of a system; they may be people, or firms, or other organisations. The edges are the connections between them. These connections could represent anything from a friendship, to a communication, to a trade deal. The power of the approach is in mapping who and what has connections, and thus influence, with whoelse and whatelse.
Nonlinearity refers to the behaviour of a system in not behaving in a linear fashion. That it is, the effect of inputs on outcomes is not proportional. This means the behaviour of a system may exhibit exponential changes, or changes in direction (i.e., increases in some measure becoming decreases) for example. Most natural and human systems are nonlinear, however linear systems are often more easily formalised and studied so they have received a lot of attention. Some research has shown that humans are better equipped to intuitively understand change as linear rather than nonlinear (i.e., we easily perceive a change, but not the rate of change, of that change).
Parameters / Parameter Space
Parameters are the measures or variables in a system. The term is often used in the context of models of a system. The model's parameters are the various input and output variables used to measure the system. The parameter space of a model is the set of all possible combinations of parameter values.
Participatory simply refers to the inclusion of people relevant to the topic we are focussing on. A decision process may be participatory if we include those that are affected by, and implement, that decision. Research may be participatory if utilise the knowledge of those who 'live' the topic we are researching. Workshops and meetings are often used to bring people together in participatory processes.
Qualitative Research Methods
Qualitative research focuses on the why and how of a topic, as opposed to the who, what, when or where. It typically aims to give a 'deep' and 'rich' understanding of a topic, as opposed to a generalisable or high level overview. Methods commonly used include unstructured or semi-structured interviews and participant observation (e.g., intense involvement with the group or people being researched).
Resilience is a measure of a system's ability to 'bounce back' after shocks, failures, or large events. It is often used in relation to robustness, which refers to a system's ability to resist change, failure or shocks.
Self-organisation refers to the process by which some form of order or coordination at the higher system level is achieved through the interaction of lower-level components, that do not have this order as an explicit goal driving their behaviour. Read more in Concepts.
A serious game is a game designed with a primary purpose other than entertainment. They are commonly used in training in health care, emergency management, and defense, amongst other industries.
A stakeholder is anyone with an interest in the topic at hand. They are people (or organisations) who hold a stake in the issue.
An action or series of actions applied to a complex system for achieving a specific purpose. Steering is a continuous process which involves interacting with, monitoring and learning from the system in question. Read more in Our Approach.
Structure here refers to the way in which a system and its processes operate. It refers to who and what are connected or influence one another. Intuitively we may think of a network graph (see above) to help us represent the structure of a system.
Many things may be referred to as a 'system'. A city, an industrial sector, the whole economy, or the road network are examples of systems. Thinking of things like these as systems is intended to help us understand them more easily. The components in a system might be people, organisations such as businesses and government, or the physical environment. Complexity Science has developed over the last fifty years or so, to help us study and understand complex systems. Read more in Concepts.
Tipping points are one of the main drivers of 'nonlinear' dynamics in complex systems. These are points, or thresholds, at which significant changes in overall system behaviour can be seen. Some tipping points can also lead to irreversible changes, for example, 'runaway' climate change, in which feedback loops mean a system is unlikely to ever return to the tipping point.
Variables are measures of system properties or elements that can change. They typically have types (e.g., numbers, words), which define what values a variable can take. For example we might have a variable 'age', which is defined in years, or months. Or we might have a variable such as 'owner' which would take the value of a name referring to a person or organisation that owns something. Variables are very useful in helping us record and understand how a system is changing.