5 tools that will increase clarity in times of uncertainty

We are living in complex times, and while we cannot reduce complexity, we can improve clarity. Complexity can be simply described as decreased expectancy. A more definitive understanding: complexity is a state of unpredictability of a system due to the number of autonomous, diverse, and interconnected parts that create the system. The more diversity, autonomous parts, and interconnections there is the more uncertainty exists requiring our attention. 

Successful organizations cannot reduce complexity because they need to keep the number of business capabilities and resources higher or equal to other organizations to maintain their competitive advantage (https://requisitevariety.co.uk/what-is-requisite-variety/). More business capabilities and resources mean more complexity. 

A side effect of complexity within organizations is ambiguity. Organizations try to bring clarity by defining an organizational structure, roles, and responsibilities. They augmented that with rules and policies to create more predictable behavior in organizations. Regardless of all the effort to create predictability within an organization, unpredictability, and unclarity are still commonplace in organizations.

First, it is important to understand that complexity follows a logical path. To understand this specific type of logic you must first change your mental model from linear to non-linear or to put it another way, from one-directional to bi-directional and non-proportional cause and impact. Once you see through this lens unclarity becomes crystal clear.

Luckily, there are tools available to help which go hand in hand with this new mental model. The data provided by these tools creates a clear picture and helps to create new mental models that are crucial for bringing clarity. There are five tools that we are going to cover Causal Loops, Stocks and flows, Agent-Based Modeling, Point of View & Reference, and several People Motivation tools.

Causality Loops

The first tool is causality loops. This tool captures elements in an organization that can be accumulated into variables and plotting their effect on other variables. These causality loops define the bi-directional relationships between the variables. Impacts depicted by positive or negative lines to define negative or positive impact. Positive impacts indicate that one variable increases another, while negative impacts indicate decreasing the impact variable.

These bi-directional relationships can be direct (between two variables) or indirect (through a third variable). Their impacts create a loop between two or more variables. Depending on the line sequence two types of loops will emerge: reinforcing loops (positive lines and/or even negative lines) and balancing loops that have positive lines and an odd number of negative lines. Causal diagrams have archetypes. These are known combinations of loops that indicate a known issue in a system. Archetypes are arriving with their own best practices to fix inherent problems within systems. 

While capturing variables and their impacts, causal diagrams allow us to capture delays between two variables. These delays bring visibility to a very deceptive behavior within systems. Delays tend to blur the impact of cause on effect. The longer the delay, the harder for people to see the causality.

Causal diagrams use only nouns (variables) and are merely an indication of impact. They do not depict the people or actions that were taken to cause the impact. This is part of the mental model change already referenced. It takes time and effort to think and model this way.

[Image 1 - simple causal diagram]

Stocks & Flows

Causal diagrams bring visibility and clarity by focusing on impacts. Stock & Flow diagrams bring clarity by focusing on the flow of information and materials between variables in the organization. Think of stocks as sinks that can contain different amounts of accumulated information or materials and flow the pipes which connect the sinks and move a certain amount of material or information from one stock to another. Stock and flow diagrams explore the flows which increase visibility and clarity into complexity.

Causal diagrams can be converted to stock and flow diagrams where variables are transformed into stock, flows, or remain as variables. This identifies and defines the flow, the logic that defines the amount of information or materials, who much will be accumulated in stock, and translating from one type of information or materials to another.

We can run Stock and Flow diagrams through multiple simulations over time using data and logic to define materials and information levels in each stock and every flow. These simulations can be used to validate a model or understand how changes will impact the current state of the organization. It’s important to note that model and therefore simulation never depicts reality. They are a simplification of reality that helps us to understand a system.

[Image 2 - simple stocks and flows diagrams]

Upon completion, the Causal Loop Diagrams (CLD) and Stock and Flows Diagrams will create understanding and visibility into the non-linear nature of complexity. Although currently unconventional, as with many advances, the insights revealed will bring a new understanding of what we’ve been missing. Yet, those models are missing the way individuals are interacting and impacting change.

Agent-Based Modeling

Agent-Based Modeling (ABM) provides clarity on how people and groups (Agents) impact each other and stocks by combining data (stocks) and logic (transitions & actions) to define how an agent state and behavior change over time. ABM is based on one or more agents that have defined logic (actions or transitions), states, stocks, and variables. Agents have defined populations that follow the ABM rules of internal or cross-agent interaction. 

ABM is best run as a simulation over a set period. The results show graphs of data changes in stocks and variables, and change of population states (in metrix), or behavior of agents (movement, connections) in a network format. 

As a rule of thumb, I use ABM modeling for each critical flow within the stock & flow diagrams. This uncovers the behavior of a population over time and demonstrates how simple rules can create complexity. 

Causal Flow & Stock diagrams, along with Agent-based diagrams, provide clarity from complexity. Although they require different mental models, the three of them are extremely valuable tools.

[Image 3 - Simple ABM]

Points of Reference & Points of View

 Having different points of view for the same reality is common and a primary reason for a lack of clarity regardless of the complexity factor. In most cases, divergent points of view result from different reference points. For example, plotting time from different events in the past versus current or future events results in two versions of the same reality. Different reference points usually create confusion unless you understand what they are showing.

Team members typically disagree on the sequence, timeframe, and location of events but this only adds context. Point of reference diagrams captures each participant's reality separately and then display them simultaneously to create a clear picture.

Individual perspectives are depicted as a graph showing the X-axis is time and the Y-axis as specific events. We begin with X = 0 which represents the captured individual the point of view. The events that happened before the individual point of view will have negative values, events that happened after will have a negative value. It is important to understand though, the base points of reference from all viewpoints should be all agreed on by all participants, but they do not need to agree on the sequence or time being evaluated. 

The Y-axis represents the sequence of events based on individual recollections. In rare scenarios, we can use the Z-axis to represent location as needed. 

By connecting all the events you create a timeline that represents the why and when of individual perspectives.

After creating this depiction for all individuals, we plot them simultaneously. The parallel lines can then be removed because they represent the deviations due to different points of reference. You are then left with a graph that depicts the real disagreement and reality.

Tools to understand the motivation (Mental Models, Game Theory, and Fast & Slow Thinking)

These tools are designed to illuminate what drives people to make certain decisions or behave in a certain way. We cannot predict behavior but allow you to understand what drives people, bringing visibility and clarity to complex situations. These tools bring insight into what drives people from different perspectives. Together they create a better understanding of why reality folds in a certain way.

A mental model explains the thought process of how something works in the real world. There are multiple resources to find known mental models but, to bring clarity, we need to the mental model which depicts one action and then explains the impacts & results of said actions. Yet, this is only one step to help increase visibility.

Game theory is typically associated with mathematical models but can also be used to get high-level information to create more clarity. Game-theory defines two possible opportunities: collaboration and/or competition (which is how people interact in general). Some interactions are symmetrical, whether competitive or collaborative and others asymmetric. Capturing these interactions and the symmetry between people provides clarity for their actions.

This first understanding of interaction brings insights into the strategy used to reach the type of collaboration they ultimately chose. High-level mapping of these strategies provides unmitigated visibility. 

Fast and slow thinking is a concept that explains how our brain is working as taken from Daniel Kahneman's book Thinking Fast and Slow. Kahneman defines two types of thinking. Fast is the most common one while slow thinking rarer. Fast thinking is the retrieval of past patterns based on input and executing those patterns. This process doesn't require any brainpower. Slow thinking is the type of deep thinking that consumes energy. 

Fast thinking is as simple as the recognition of facial impressions. Slow thinking can be understood in the context of the outcome of asking someone to compute 345 x 345. People instinctually use fast thinking for most daily tasks. Few use slow thinking. Knowing which mental process is being used for each task brings valuable clarity.