Action Science a field of inquiry developed by Chris Argyris and Donald Schon "aimed at exploring the reasoning and attitudes which underlie human action, and producing more effective learning" in individuals, organizations, and other social systems. (TFDH, p.237) The Action Science theory was designed to promote reflection and inquiry into the reasoning behind our actions (TFD, p.82). Action Science critiques traditional social science when the experimenters remain "aloof" from the experiment. (TFDH, p.266) Also, action science assumes that there is a theory-in-use or mental model behind every action, a type of logic that happens inside one’s mind.

Action Inquiry is another branch of Action Science developed by William Torbert that describes several types of these theories in use, called action-logics. In fact, he was the student of Chris Argyris and coined the term Action Science in his graduate work. Most practitioners use these terms interchangeably.

Mental models are a core concept in Action Science. Mental models often get us into trouble when they are untested, this is particularly true in groups settings when everyone is walking around with their own ideas of how the world works without sharing them with others (similar to MBTI types). Argyris describes two major types of mental modes, which he calls model I and model II. Torbert breaks these models/action-logics: Opportunist, Diplomat, Expert, Achiever, Leader, Individualist, Strategist, Magician, & Ironist. For each author the models/action logic represents a stage of development that one passes as the mature and each stage incorporates a unique mental model.

The Ladder of Inference is an exercise that helps us: 1) become aware of our own thinking and reasoning and 2) make our thinking and reasoning more visible to others (advocacy). Participants in this exercise learn how to move from observable "data" and experiences, to selected "data", added meanings, assumptions from the data & meanings, conclusions that are drawn, beliefs from these conclusions, and actions based on these beliefs. Not surprisingly, by participating in this exercise we see how our beliefs affect what data we select to see next time.

An example of the ladder of inference can be see in the example below.

Belief: This professor is prejudiced against athletes. Conclusions: The professor picks on Jane because she is an athlete. Selective Data: The professor is chewing Jane out. Observable Data: "Jane your performance is not up to standard," says the professor.

It is impossible to not draw meanings from the world around us. Analyzing every action in the ladder of inference model would be overwhelming and tedious. This is a useful way of improving communications skills. (TFDH, p. 245)

Recognizing "leaps of abstraction" from the actual data of the real experience to an attribution or generalization about what happened without testing. Leaps of abstraction happen because we think so quickly, but often this is a challenge for learning because we "leap" to generalization very quickly. For example, we’ve often heard people say something like "Pat doesn’t care about people." This is based on inferences, since Pat probably never said "I don't care about people". Failing to distinguish direct observation from generalizations from inferred observation leads us never to think to test the generalization. Often people don’t ever care to test assumptions, but this can yield new information. For instance, it may actually be the cast that Pat has a hearing impediment and can’t participate in office conversations well since people speak quickly.

Leaps of abstraction occur often in business. For instance ideas like "competition orientation is better than customer orientation" was prevalent among top managers in most firms in the US before the quality movement. Another example is the common belief (industry wide shared mental model) that being to market first is a key to success. When the Apple III was released before enough testing. However, this turned out to be a disappointment to consumers and business people and developed a reputation of unreliability for that product.

Key questions for leaps of abstraction:

"What is your ‘data’ on which this generalization is based?"

This is a great technique for articulating what we normally do not say. It is useful to use when you can’t reach agreement, are with people now putting in their time, are being treated unfairly, and your organization is opposing your change initiative. It consists of taking a piece of paper and drawing a line down the middle and write on one column "What I’m thinking" and "What is said" in the other. Then you write out a recent challenging dialogue that actually occurred..

Scenario: Getting feedback on a paper a professor didn’t like

The goal of Action Science/Inquiry is to promote "reflection in action" the ability to identify the dynamics of a situation and comment on them as they unfold in a conversation by offering direct advocacies and questions (inquiries) into the discussion. To uncover "mental models", through exercises like the ladder of inference or left-hand column, we are able to understand the difference between our espoused theories (what we say and believe as an ideal) and theories-in-use (implied theory in what we actually do).

Learning how to use these skills can lead to new learning. Action Science practitioners often try to create double-loop learning. This occurs by improving outcomes through understanding our actions and beliefs. Normally, most people operate out of a single-loop learning which seeks to improve the relationship between outcomes and behaviors without questioning or understanding the beliefs guiding the situation.

Sources:

Senge, et al. The Fifth Discipline Handbook

Senge, et al. The Fifth Discipline

Fisher, Rooke, & Torbert, Personal and Professional Transformation