Ask Agger - How Behavioral Design Helps Make Better Games and How Games Can Be Used in Behavioral Design

1. FROMLEARNINGTODOING AskAgger SeriousPlayConference,July19,2017

2. HOW CAN BEHAVIORAL DESIGN HELPS US MAKE BETTER GAMES? AND HOW CAN GAMES BE USED IN BEHAVIORAL DESIGN?

18. Workz A/S © 2017

19. Workz A/S © 2017

20. Workz A/S © 2017 Workz A/S © 2017

21. MY POINT OF DEPARTURE MY POINT OF DEPARTURE… …

22. Most organizations are embarrassing bad at handling change and transformations: • 74% of change projects are unsuccessful. • 15-30% of change projects are complete waste. • Amongst the senior executives that are being replaced, one third is replaced due to unsuccessful transformation initiatives (not due to lack of strategy). • At the state of completion most transformation initiatives has replaced senior management (Program Lead as well as Steering Committee executives) 2-3 times. Workz A/S © 2017 Sources: McKinsey & Co Global Survey, IBM, Fortune, Deloitte Research etc.

23. In the end all our learning, development and change activities are about changing behavior. If new meaning, knowledge, wisdom and skills doesn’t translate into ordinary work day behavior they are a lost investment. It only matters what we actually do and gets done. Workz A/S © 2017

24. The question: Why are we, as individuals and organizations, so bad at turning good intentions, knowledge and learnings into real life changes in behaviors? Workz A/S © 2017

25. Behavioral science’s answer: Because this is how we have been designed! Workz A/S © 2017

28. BEHAVIOURAL DESIGN

30. EVOLUTION HAS DESIGNED US TO BE: EVOLUTION HAS DESIGNED US TO BE: 1. CONSERVATIVE Westicktothebehaviourthatweknowbest(andwhichhas insuredoursurvivalinthepast). Workz A/S © 2017

31. Behavioural Public Policy Behavioural Public Policy 6 6 Two of the versions of the experiment involved a skin-rash treatment. In these versions, subjects were advised that “[m]edical researchers have de- veloped a new cream for treating skin rashes.” They were also advised that “[n]ew treatments often work but sometimes make rashes worse,” and “skin rash- es sometimes get better and sometimes get worse on their own” whether or not a person is treated. To determine the effect of the new treatment, ex- perimenters (the subjects were told) had divided a number of patients suffering skin rashes into two groups—one that was administered the skin cream, and another that was not—and then observed in how many the skin condition improved and how many it got worse after a two-week trial period. Based on the results, as reflected in the 2x2 contin- gency table, subjects were instructed to indicate whether the “[p]eople who used the skin cream were more likely to get better than those who didn’t” or instead “People who used the skin cream were more likely to get worse than those who didn’t.” ment of causal inference that confounds even many intelligent people (Stanovich 2009; Stanovich & better” and “Rash got worse”—were manipulated Two of the versions of the experiment involved a skin-rash treatment. In these versions, subjects were advised that “[m]edical researchers have de- veloped a new cream for treating skin rashes.” They were also advised that “[n]ew treatments often work but sometimes make rashes worse,” and “skin rash- es sometimes get better and sometimes get worse on their own” whether or not a person is treated. To determine the effect of the new treatment, ex- perimenters (the subjects were told) had divided a number of patients suffering skin rashes into two groups—one that was administered the skin cream, and another that was not—and then observed in how many the skin condition improved and how many it got worse after a two-week trial period. Based on the results, as reflected in the 2x2 contin- gency table, subjects were instructed to indicate whether the “[p]eople who used the skin cream were more likely to get better than those who didn’t” or instead “People who used the skin cream were more likely to get worse than those who didn’t.” The two versions of the skin-treatment treat- ment problem differed only with respect to which result the experiment supported. The numbers in the 2x2 contingency table were kept the same, but the labels at the tops of the columns—“Rash got people use one of two heuristic alternatives to this better” and “Rash got worse”—were manipulated (Figure 3). (Figure 3). Correctly interpreting the data was expected to be difficult. Doing so requires assessing not just the absolute number of subjects who experienced posi- tive outcomes (“rash better”) and negative ones (“rash worse”) in either or both conditions but ra- ther comparing the ratio of those who experienced a positive outcome to those who experienced a neg- ative one in each condition. Comparing these ratios is essential to detecting covariance between the treatment and the two outcomes, a necessary ele- ment of causal inference that confounds even many intelligent people (Stanovich 2009; Stanovich & West 1998). Correctly interpreting the data was expected to be difficult. Doing so requires assessing not just the absolute number of subjects who experienced posi- tive outcomes (“rash better”) and negative ones (“rash worse”) in either or both conditions but ra- ther comparing the ratio of those who experienced a positive outcome to those who experienced a neg- ative one in each condition. Comparing these ratios is essential to detecting covariance between the treatment and the two outcomes, a necessary ele- ment of causal inference that confounds even many intelligent people (Stanovich 2009; Stanovich & West 1998). Based on previous studies using the design re- flected in this experiment, it is known that most people use one of two heuristic alternatives to this approach. The first involves comparing the number of outcomes in the upper left cell to the number in the upper right one (“1 vs. 2”). The other (“1 vs. 3”) involves comparing the numbers in the upper left and lower left cells (Wasserman, Dorner & Kao 1990). Behavioural Public Policy 6 Two of the versions of the experiment involved a skin-rash treatment. In these versions, subjects were advised that “[m]edical researchers have de- veloped a new cream for treating skin rashes.” They were also advised that “[n]ew treatments often work but sometimes make rashes worse,” and “skin rash- es sometimes get better and sometimes get worse on their own” whether or not a person is treated. To determine the effect of the new treatment, ex- perimenters (the subjects were told) had divided a number of patients suffering skin rashes into two groups—one that was administered the skin cream, and another that was not—and then observed in how many the skin condition improved and how many it got worse after a two-week trial period. Based on the results, as reflected in the 2x2 contin- gency table, subjects were instructed to indicate whether the “[p]eople who used the skin cream were more likely to get better than those who didn’t” or instead “People who used the skin cream were more likely to get worse than those who didn’t.” better” and “Rash got worse”—were manipulated (Figure 3). Correctly interpreting the data was expected to be difficult. Doing so requires assessing not just the absolute number of subjects who experienced posi- tive outcomes (“rash better”) and negative ones (“rash worse”) in either or both conditions but ra- ther comparing the ratio of those who experienced a positive outcome to those who experienced a neg- ative one in each condition. Comparing these ratios is essential to detecting covariance between the treatment and the two outcomes, a necessary ele- Based on previous studies using the design re- flected in this experiment, it is known that most people use one of two heuristic alternatives to this approach. The first involves comparing the number of outcomes in the upper left cell to the number in the upper right one (“1 vs. 2”). The other (“1 vs. 3”) involves comparing the numbers in the upper left and lower left cells (Wasserman, Dorner & Kao 1990). POLITICS MAKES US BAD AT MATH ment problem differed only with respect to which result the experiment supported. The numbers in the 2x2 contingency table were kept the same, but the labels at the tops of the columns—“Rash got approach. The first involves comparing the number of outcomes in the upper left cell to the number in the upper right one (“1 vs. 2”). The other (“1 vs. 3”) involves comparing the numbers in the upper left and lower left cells (Wasserman, Dorner & Kao 1990). The two versions of the skin-treatment treat- West 1998). Based on previous studies using the design re- flected in this experiment, it is known that most Behavioural Public Policy 12 Liberal Democrat (-1 SD on Conservrepub) Conservative Republican (+1 SD on Conservrepub) low numeracy = 3 correct/ high numeracy = 7 correct Low numeracy High numeracy The two versions of the skin-treatment treat- ment problem differed only with respect to which result the experiment supported. The numbers in the 2x2 contingency table were kept the same, but the labels at the tops of the columns—“Rash got rash decreases rash increases rash increases rash decreases rash decreases rash decreases rash increases Skin treatment rash increases 0 1 2 3 4 5 6 7 8 9 1 0 0% 1 2 3 4 5 6 60% 70% 80% 7 8 9 1 0% 10% 20% 30% 40% 50% probabilityofcorrect interpretation of data 60% 70% 80% 10% 20% 30% 40% 50% probability of correct interpretation of data 90% 100% 90% 100% crime decreases crime increases crime increases crime decreases crime decreases crime increases crime decreases crime increases Gun ban Figure 3. Experimental conditions. Subjects were assigned to one of four conditions. The conditions are identified by labels (A)-(D) in a manner that indicates the result or outcome of the experiment that is most supported by the data contained in the rele- vant table. The correct interpretation of the data was manipulated by varying the result specified by the headings above the columns. 0 1 2 3 4 5 6 7 8 9 1 Figure 3. Experimental conditions. Subjects were assigned to one of four conditions. The conditions are identified by labels (A)-(D) in a manner that indicates the result or outcome of the experiment that is most supported by the data contained in the rele- vant table. The correct interpretation of the data was manipulated by varying the result specified by the headings above the columns. 0% 10% 20% 30% 40% 50% 60% 70% 80% 0% 0% 10% 10% 20% 30% 40% 50% 60% 70% 80% 60% 70% 80% 90% 100% 90% 100% 90% 100% 20% 50% 30% 40% probability of correct interpretation of data probability of correct interpretation of data Figure 7. Predicted probabilities of correctly interpreting data. Density distributions derived via Monte Carlo simulation from regression Model 3, Table 1, when predictors for Conserv_Repub set at -1 SD and +1 SD for “Liberal Democrat” and “Conservative Republican,” respectively, and numeracy set at 3 and 7 for “low numeracy” and for High numeracy, respectively (King, Tom & Wit- tenberg 2000). Workz A/S © 2017 Source:DanM.Kahn,EllenPeters,EricaCantrellDawson&PaulSlovic:MotivatedNumeracyandEnlightenedSelf-Government. ports the conclusion that a gun ban decreases crime, but is less likely to correctly identify the outcome when the data supports the conclusion that a gun ban increases crime. This pattern of polarization, con- trary to the SCT hypothesis, does not abate among high-numeracy subjects. was attributable to ideologically motivated reason- ing, there were no meaningful or significant partisan differences among high-numeracy subjects—or low-numeracy ones, for that matter—in the skin- treatment conditions (Figure 8). These findings sup- port the third, ITC hypothesis. Figure 3. Experimental conditions. Subjects were assigned to one of four conditions. The conditions are identified by labels (A)-(D) in a manner that indicates the result or outcome of the experiment that is most supported by the data contained in the rele- vant table. The correct interpretation of the data was manipulated by varying the result specified by the headings above the columns. Indeed, it increases. On average, the high numer- acy partisan whose political outlooks were affirmed by the data, properly interpreted, was 45 percentage points more likely (± 14, LC= 0.95) to identify the conclusion actually supported by the gun-ban exper- iment than was the high numeracy partisan whose political outlooks were affirmed by selecting the incorrect response. The average difference in the case of low numeracy partisans was 25 percentage points (± 10)—a difference of 20 percentage points (± 16). Corroborating the inference that this effect The reason that numeracy amplified polariza- tion, these analyses illustrate, was that high numera- cy partisans were more likely than low numeracy ones to identify the correct response to the covari- ance-detection problem when doing so affirmed subjects’ political outlooks. A high-numeracy Con- servative Republican, the model predicted, was 21 percentage points (± 16) more likely than a low- numeracy one to recognize the correct result in the “crime increases” condition; in the “crime decreas- es” condition, a high-numeracy Liberal Democrat

32. EVOLUTION HAS DESIGNED US TO BE: EVOLUTION HAS DESIGNED US TO BE: 1. CONSERVATIVE Westicktothebehaviourthatweknowbest(andwhichhas insuredoursurvivalinthepast). 2. IRRATIONAL Onlyalimitedpartofourbehaviourisbasedonrationaland consciouschoices.Werunonautopilot.Ourwillpowerisa limitedresource. Workz A/S © 2017

33. JUDGES MAKE DECISIONS WITH THEIR STOMACHS Source:DanielKahneman

34. FAST AND SLOW THINKING Source:DanielKahneman

35. EVOLUTION HAS DESIGNED US TO BE: EVOLUTION HAS DESIGNED US TO BE: 1. CONSERVATIVE Westicktothebehaviourthatweknowbest(andwhichhas insuredoursurvivalinthepast). 2. IRRATIONAL Onlyalimitedpartofourbehaviourisbasedonrationaland consciouschoices.Werunonautopilot.Ourwillpowerisa limitedresource. 3. DELUSIONAL Wearegreatatconvincingourselvesandothersthatourchoices andbehavioursarerationalandintentional. Workz A/S © 2017

36. WE LIE TO OURSELVES TO FEEL IN CONTROL WE LIE TO OURSELVES TO FEEL IN CONTROL Workz A/S © 2017 Spource:DanielKahneman

37. MY SUSPICION: Our approach to management, HR and training is still mainly founded on the illusion of “economic man”. We are highly optimistic (or delusional) when it comes to our belief in our power to act like we intend. Internally, our organizations are not designed to support behavioural change. • • • Workz A/S © 2017

38. How can complex organizations improve their ability to transfer learning and training efforts into tangible behavioral changes? Workz A/S © 2017

39. NEW BEHAVIOUR = MOTIVATION x ABILITY x TRIGGER High MOTIVATION TRIGGERS Low Hard Easy ABILITY Workz A/S © 2017 Source: BJFogg

41. HOW CAN BEHAVIORAL DESIGN HELPS US MAKE BETTER GAMES? AND HOW CAN GAMES BE USED IN BEHAVIORAL DESIGN?

42. HOW CAN BEHAVIORAL DESIGN HELPS US MAKE BETTER GAMES? 1. Remind us to focus on behavioural changes instead of only new knowledge or skills. Workz A/S © 2017

43. SAFETY CULTURE AND BEHAVIOUR Workz A/S © 2017 Workz A/S © 2017

44. HOW CAN BEHAVIORAL DESIGN HELPS US MAKE BETTER GAMES? 1. Remind us to focus on behavioural changes instead of only new knowledge or skills. Help us to distinguish between influencing motivation, ability or triggers. Most learning games lack any connection to triggers. 2. Workz A/S © 2017

45. BOOSTING MOTIVATION Workz A/S © 2017 Workz A/S © 2017

46. HELPING EVERY DAY DECISION MAKING Workz A/S © 2017 Workz A/S © 2017

47. HOW CAN BEHAVIORAL DESIGN HELPS US MAKE BETTER GAMES? 1. Remind us to focus on behavioural changes instead of only new knowledge or skills. Help us to distinguish between influencing motivation, ability or triggers. Most learning games lack any connection to triggers. Emphasise the importance of balancing the level of stress and complexity during game-play. 2. 3. Workz A/S © 2017

48. GETTING INTO THE POOL Workz A/S © 2017 Workz A/S © 2017

49. HOW CAN BEHAVIORAL DESIGN HELPS US MAKE BETTER GAMES? 1. Remind us to focus on behavioural changes instead of only new knowledge or skills. Help us to distinguish between influencing motivation, ability or triggers. Most learning games lack any connection to triggers. Emphasise the importance of balancing the level of stress and complexity during game-play. Provides a toolset for optimising (“manipulating”) the training experience, especially regarding group dynamics. 2. 3. 4. Workz A/S © 2017

50. HANDLING THE DOMINANT ALPHAS Workz A/S © 2017 Workz A/S © 2017