Outcomes Theory Informing DoView Planning
See. Plan. Do.
(also see Detailed Theory Page and the Handbook)
Quick Overview
DoView Planning is part of an overall approach:
DoView Planning is the methodology.
DoView Boardsare the tool.
Outcomes Theory is the conceptual foundation (this page).
The Outcomes Society, Outcomes Organization and Outcomes-Transparent AI is the vision.
Outcomes theory was developed by outcomes specialist Paul Duignan, PhD.
If you want a very quick introduction to outcomes theory, watch the three-minute video below.
Look at the other three-minute videos at the bottom of this page.
If you want in-depth information about it, read Duignan’s seminal 2009 paper describing outcomes theory in detail in the context of evaluation and performance management.
Search the DoView Planning and Outcomes Theory Handbook for 100+ tools based on outcomes theory.
See the detailed theory page.
OUTCOMES THEORY
Outcomes Theory provides the conceptual foundation for DoView Planning and DoView Boards.
It gives a more conceptually coherent way of thinking about outcomes, pathways, actions, evidence, measurement, reporting and accountability than the current mish-mash of concepts drawn from different disciplines.
It is designed for any situation where humans, organizations or AI systems are trying to act in the world.
WHY THEORY MATTERS
Outcomes work is often conceptually confused.
People from many different disciplines are involved (e.g., planning, management, accounting, contracting, operations, measurement and evaluation). They often use terms in different ways.
Outcomes Theory helps conceptually by moves such as:
defining outcomes and steps by their features and position within the relevant ‘outcomes hierarchy’ (e.g. the This-Then pages within a DoView Board) instead of by their names (e.g. activities, outcomes, goals). More from book.
specifying both high-level outcomes and the logical pathway of steps it is believed will lead to them (intervention logic, theory of change). More from book.
insisting on separating a measure from the outcome or step it is attempting to measure. More from book.
formally specifying the components of any outcomes system. More from book.
providing a framework for specifying the type of reporting and accountability required of an intervention. More from book.
The handbook provides more than 100 tools that provide conceptual clarity on all aspects of outcomes work. Search it here.
CORE IDEA
AN ‘OUTCOMES SYSTEM’
Any attempt to act in the world can be understood as an ‘outcomes system’.
An implicit or explicit outcomes system sets out:
the outcomes being sought
the logic of the pathways being taken to those outcomes
the assumptions assumed and risks being managed
the actions being taken
the evidence being used
the measures being tracked
the accountabilities being assigned
Outcomes systems are made explicit by using:
DoView Planning as a practical visual methodology for working with outcomes systems.
DoView Boards are a tool for representing any outcomes system.
USE WITH AI
“[AI’s] a force multiplier, and force without direction is just noise’”
AI is making it easier to produce more activities, outputs, and options.
As AI increases the scope of what we can potentially do, it is essential that we have a disciplined way of clearly specifying the outcomes we are seeking. We also need to have a way of ensuring that AI takes the steps that are most likely to lead to such outcomes. and effectively manages the associated risks.
To do this properly, we need a comprehensive and robust conceptual framework that applies to both human and AI agents. Outcomes Theory provides such a framework.
See the Outcomes Society, the Outcomes Organization and Outcomes-Transparent AI.
DoView Drawing Rules
In outcomes theory, outcomes hierarchies (intervention logics, theories of change, the This-Then pages within a DoView Board) are drawn according to a specific set of rules.
These rules are central to outcomes theory and DoView Planning.
They were developed in the course of modeling thousands of real-world projects.
They provide a comprehensive, user-friendly, conceptually sound visualization of the outcomes, the steps for pursuing them, the assumptions and the risks related to any attempt to act on the world.
The rules
Keep boxes within the DoView diagrams as short as possible for quick reading.
Name boxes as ‘outcomes’ – in the sense of completed states rather than as ongoing ‘processes’. Change the wording of a box from ‘…ing’ to ‘…ed’. For example, change ‘increasing stakeholder support’ to ‘increased stakeholder support’.
Only include a single concept in each box – e.g. don’t put ‘achieve A by doing B’ because such a box includes both an end and a means in the same box. For example, do not have a box reading ‘increased awareness through producing a video’; break this box up into two separate boxes. Doing this encourages consideration of alternative ways of achieving the higher-level box, which is ‘increased awareness’.
Build externally-focused, not organizationally-focused, DoView diagrams. Your thinking should go beyond just the things that an initiative is doing and things that are controllable by the initiative. The DoView should show all of the things that need to happen to achieve high-level boxes. This should include risks (written in the positive) and assumptions included within the same model. This means that a reader can see everything that needs to happen within the same DoView rather than having to look elsewhere for things like assumptions (for instance, in a separate table within planning documentation).
You do not need to divide a DoView diagram up into sections such as inputs, activities, outputs and outcomes. Allow the causal flow in the outside world to determine the position of the boxes within its structure. After you have built a DoView, if you want, you can then tag boxes within it with any of the above categories that you want to.
Do not siloize DoViews by requiring that a lower-level box can only contribute to a single higher-level box.
Keep measurement separate from the boxes that are being measured by mapping measures (indicators) onto boxes in the DoView after the diagram has been built. This means you can represent currently unmeasured boxes in your DoView. It also raises awareness that a measure is a measure of a box rather than necessarily being the box itself. This approach highlights that the way a box is being measured may be able to be improved in the future.
Future-proof the DoView by building one that shows everything that needs to happen over time. Then identify current priorities onto the DoView. This is much more efficient and transparent than just creating a DoView that only contains current priorities. It allows the selection of current priorities to be critiqued by just looking at the other boxes in the DoView that have not been prioritized at the particular point in time. It also makes strategic planning faster because you can rapidly change the priority of particular boxes rather than having to rewrite your strategic plan each time priorities change.
Do not assume that an organization needs a single high-level outcome. If it is working on multiple issues that are differentiated in the outside-world, it is appropriate for it to have multiple high-level outcomes.
Put outcomes into a left-to-right format with the highest-level outcomes on the right. This is a discretionary rule, and you can build bottom-to-top outcomes models, but for languages that work from left to right, a left-to-right model is generally easier for them to process.
Include all relevant and necessary steps at each level within the model.
If appropriate, put an evaluative descriptor such as adequate or sufficient in front of your steps and outcomes.
Make your DoView Diagrams as large as they need to be to capture the organization or initiative by dividing them up into meaningful sub-pages, creating a multi-layered model.
DIG DEEPER
“[AI’s] a force multiplier, and force without direction is just noise’”
Search the DoView Planning and Outcomes Theory Handbook.
See the detailed Theory page.
Watch the three-minute videos below.
Subscribe to the Outcomes Theory Substack below.
Outcomes Theory Substack
Outcomes theory offers a comprehensive framework for conceptualizing and working with outcomes systems of any type. Outcomes systems are systems attempting to identify outcomes and effect change. Outcomes theory encompasses a set of widely applicable definitions and principles that span the various terms employed by different disciplines involved in outcomes work. Its insights can be applied to human agents, AI agents, or both human and AI agents working together.
Difference between an outcome and an output
Three-minute videos on aspects of outcomes theory relevant to DoView Planning
What is outcomes theory?
Discusses the differencebetweenanoutcome and an output. An output is controllable by a human or AI agent. This means that merely measuring it has occurred means that we can assume it occurring is attributable to the particular agent. This is not the case with higher-level outcomes where merely measuring outcomes often does not guarantee that changes in them can be attributed to a particular agent because such high-level outcomes are often influenced by a number of other factors. Always discussing outputs and outcomes against a DoView diagram makes discussions about the different level of outputs and outcomes much clearer.
Building better KPI and indicator sets
When developing indicator sets for human or AI agents within any type of outcomes system, it is essential to do so against a visual DoView outcomes model that outlines all the high-level outcomes being sought. If you don’t you have no way of knowing if your indicator set comprehensively measures the full set of outcomes and steps that you are trying to change in the outside world. To assess an indicator set, people have to implicitly build a mental outcomes model. Why not develop one in the form of a DoView and use it to build your indicator set from the beginning?
What are outcomes models (DoViews)?
Outcomes models, strategy diagrams or DoViews lie at the heart of outcomes theory. They are both a coneptual and practical tool. They set out in a visual format all of the high-level outcomes being sought by any outcomes system and all of the lower level steps that may be used to influence them. Outcomes models are drawn according to the DoView conventions, which means they are a generic way of modeling outcomes in any sector, and because of this, parts of them can be reused in different contexts.
Escaping the terminological madness in outcomes work
When dealing with outcomes systems (any system that attempts to specify outcomes and acts to change them), there are often a lot of different terms being thrown around which are interpreted in various, sometimes confusing, ways. Because it is based on a rigorous set of definitions, outcomes theory allows us to clearly specify from a conceptual point of view exactly what is meant by the use of a particular term such as outputs, outcome, goal, objective etc.
Identifying evaluation questions
In any outcomes system, assessing its impact is crucial. Just like with indicators, mapping evaluation questions to an outcomes system’s Doview outcomes model is vital. Evaluation questions exist at various levels within an outcomes model, and the same question can be phrased differently, leading to confusion. Determining the level where different evaluation questions lie is significantly easier using a visual outcomes model rather than attempting to do it mentally or discussing it solely in writing. The current discussions about the appropriateness of various AI benchmarks would benefit by mapping them onto an appropriate outcomes model.
Gaming indicators when used for incentivization
A concrete example of an outcomes theory principle. ‘The more an indicator is used for incentivization, the less accurate it will be. This applies to organizations but equally to AI systems. An AI’s ‘reward function’ is analogous to a performance indicator and just like some Wells Fargo employees AI systems can game their rewards which are being used to train them. So in AI systems as in organizational systems one needs to risk-manage around manipulability when a human or AI agent attempts to get a ‘reward’ in a way that is not aligned with the intentions of those who set the system up.
Assess versus assist mode in delegation and contracting
Difference between an assess mode and an assist mode in an outcomes system. If you have an outcomes system with a sole available provider focusing on one part of your DoView outcomes model, then you also need to have an assist mode for that provider. This is because, in comparison to situations where you could quit that provider and there are many others that could take its place, if there is only one available provider, you need to help them improve when they fail an assessment.
Different types of evaluation - implementation, impact etc.
Evaluation comes in various forms, and these can be positioned along the continuum of a DoView outcomes model. While evaluation often focuses solely on evaluating high-level outcomes, it is essential to recognise other types as well. These include implementation evaluation at an initiative's outset, process evaluation that looks at context and execution, impact/outcomes evaluation, summative evaluation, and economic evaluation. Given the current interest in evaluating AI systems, we need to consider employing all these evaluation types.