Action science skills for managing down, across or up the ladder

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Action science skills for managing down, across or up the ladder Pacey C. Foster Organization Studies Department Boston College

Conclusion Most of the time, we treat our interpretations as facts, rather than as hypotheses. By treating our interpretations as hypotheses that require testing, we can increase our effectiveness at managing down, across or up the ladder. Implementing this advice requires a shift in attitude and behavior (e.g. approaching management like a behavioral scientist)

Overview Generate and analyze first person data Introduce two new theoretical tools -Ladder of inference -Balancing parts of speech Analyze the reading using the tools Connections to your own work experience

Cognitive Biases and Heuristics Ease of recall and availability Events that are vivid, evoke emotion and/or are specific are easier to remember. Representativeness Events that match preexisting mental categories are seen as more likely than those that don t (regardless of underlying likelihood, base rates, etc.) Anchoring and adjustment Our conclusions are biased by prior data (regardless of its relevance, new information, etc.)

The problem Our perceptual filters and heuristics are biased Our brains are huge data reduction mechanisms Heuristics and mental models are a central technique our brains use to reduce data Our heuristics are biased and self serving (e.g., fundamental attribution error) We are usually unaware of the influence of heuristics on our perceptions, therefore... We underestimate the extent to which our conclusions may be wrong

The problem refined We treat our own interpretations as facts We rarely test our interpretations (or the interpretations of others) Most of the time, this process is happening just below our level of awareness

The Ladder of Inference (Senge, TFDR, p. 242) We live in a world of self generating beliefs which remain largely untested Our reasoning is self sealing Assumptions that limit effectiveness Our beliefs are the truth The truth is obvious Our beliefs are based on real data The data we select are the real data

Self Sealing Reasoning Process 1. We select data that is biased 2. We add meaning and make assumptions based on our own experience, culture, etc. 3. We draw conclusions 4. We take actions based on these conclusions 5. People s reactions reinforce our conclusions and affect what data we select next time

Self Sealing Reasoning Example I say hello when I pass you on the way to class. You do not reply. (Observable data) Nice people reply when people greet them. Since you didn t, you must not be very nice. (Meaning making and inferences) I don t reply to you when you say hello to me in class next week. (My actions) You conclude I am not nice and stop talking to me. (Your reactions confirm my conclusions)

Using the Ladder of Inference To become more aware of your own thinking and reasoning To make your thinking and reasoning more visible to others To inquire into others thinking and reasoning (Senge, TFDR, p. 245)

The Ladder of Inference Language Clarifying data selection What is the observable data behind that statement? Does everyone agree on what the data is? Making meaning making visible Can you run me through your reasoning? How did you get from x, y, z data to x, y, z conclusion? Publicly testing inferences Larry, I noticed that you have been quiet during the meeting. (Naming data) Does that mean you are uninterested? (Testing inference)

Advocacy & Inquiry 2 x 2 HIGH ADVOCACY LOW Imposing Explaining Observing Withdrawing Mutual Learning Interrogating Interviewing LOW HIGH INQUIRY

Action science applied McNeil and Walters case (from the reading) McNeil s performance problems Walter s response Adario s reaction Ladder of inference analysis Parts of speech analysis

Action science applied A case from your own experience? Recent list serve comment about the Indian Restaurant Others?

Action science: A new approach Treat our own (and others ) conclusions as hypotheses to be tested Two tools of action science Ladder of inference Balancing advocacy and inquiry Practice using the tools off line Apply the tools in practice to increase your effectiveness