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Behavior Lab. T-A-M-U, like

1:07

Welcome to episode 472 of

1:09

the Brainy Business, understanding the

1:11

psychology of why people buy. Today's

1:14

episode is all about

1:16

achieving hyperperformance with Dr. Agnes

1:18

Steeb. You

1:25

are listening to the Braney Business

1:28

Podcast, where we dig into

1:30

the psychology of why people

1:32

buy and help you incorporate

1:34

behavioral economics into your business,

1:36

making it more brain-friendly. Now

1:38

here's your host, Molina Palmer.

1:41

Hello, hello everyone. My name is

1:43

Molina Palmer, and I want to

1:45

welcome you to the Braney Business

1:47

Podcast. One of my favorite case

1:49

studies to share is of a

1:51

company called The Littery. Their CEO

1:54

joined me on the podcast way

1:56

back in episode 75 and talked

1:58

about the way they used behavioral

2:00

economics to reframe the problem of

2:02

getting people to properly throw away

2:04

and sort their garbage by looking

2:07

at motivation and incentives differently and

2:09

inventing smart garbage cans that have

2:11

allowed them to turn litter into

2:13

lottery tickets. It's an amazing story

2:15

and one that my guest today,

2:18

Dr. Agnes Steve, was a big

2:20

part of in the behavioral aspects

2:22

that went into that company. He

2:24

will be talking about that work

2:26

and many other amazing things he

2:29

does in our conversation today, which

2:31

originally aired back in June of

2:33

2022. He's a fascinating person with

2:35

some really great case studies. I

2:38

can't wait to share them with

2:40

you. Don't forget links for my

2:42

top related past episodes and books

2:44

are waiting for you in the

2:46

show notes for this episode which

2:49

are found within the app you're

2:51

listening to and at the brainy

2:53

business.com/four seven two All right, let's

2:55

jump right in dr. Agnes Steve.

2:57

Welcome to the brainy business podcast

3:00

Happy to be here. Let's see

3:02

you again. Absolutely always delighted to

3:04

see and chat with you and

3:06

For everyone who is not yet

3:08

familiar with you and your amazing

3:11

work, can you share a little

3:13

bit of your background and the

3:15

work that you do? I'm really

3:17

passionate about helping people, teams, organizations,

3:19

and societies to get where they

3:21

want to get and usually they

3:24

want to get to a higher

3:26

level of satisfaction, whether through the

3:28

well-being of society or that's through

3:30

the performance in organization or just

3:32

individuals to boosting their self-esteem. So

3:35

how do we get there? I

3:37

think it's a good to combine

3:39

the resources of technology that we

3:41

currently are developing, especially the most

3:43

promising one artificial intelligence and craft

3:46

it with the understanding of how

3:48

humans are and how humans can

3:50

change, or more specifically, how humans

3:52

are not willing to change and

3:54

instruct the artificial intelligence to help

3:56

us to get where we want

3:59

to get. I label it as

4:01

hyperperformance. And what do I mean

4:03

by that is not by adding

4:05

more skills, practice, or knowledge, but

4:07

removing the obstacles from human thinking.

4:10

It's our counterproductive psychology that is

4:12

oftentimes the roadblock to our own

4:14

success and happiness. individual at societal

4:16

and also organizational levels. And I'm

4:18

doing research in that, I'm teaching

4:21

subject related to that, I also

4:23

give keynotes and also master classes

4:25

for organizations to help embrace that

4:27

perspective because unfortunately the history tells

4:29

and the habits are in especially

4:32

in organizations that is, oh we

4:34

all people are not performing very

4:36

well, our KPIs are quite low.

4:38

Maybe we should send to the

4:40

training. So after training, there will

4:42

be different people. Now, my good

4:45

friend from when I worked at

4:47

Hewlett-Packard said, you know, a fool

4:49

with a tool is still a

4:51

fool. So I took that perspective

4:53

and I tried to tailor it

4:56

from the perspective if there is

4:58

a bias in employees or presidents

5:00

or individuals somewhere. the bias is

5:02

still there no matter how well

5:04

you train the person. So that's

5:07

kind of the comparison between increased

5:09

performance and hyper performance that I'm

5:11

really passionate about. So that's my

5:13

journey. And with that, where you

5:15

say, you know, the fool with

5:17

the tool is still a fool,

5:20

right? And I believe what I'm

5:22

hearing and what you're saying there

5:24

is, like, even though, even when

5:26

we know about the biases, we

5:28

can't fully eliminate them. It's not

5:31

like, oh, I'm not going to.

5:33

stereotype this way anymore. I'm not

5:35

going to have confirmation bias be

5:37

a thing for me, right? We

5:39

don't get to just turn that

5:42

off when we're aware of them

5:44

unfortunately. So what sort of tips

5:46

and things do you incorporate into

5:48

the work that you're doing to

5:50

help people be a little less

5:52

of a fool with their tools.

5:55

Okay, it's kind of interesting to

5:57

have like a very simplified version

5:59

of it, but I expressed that

6:01

a friend of mine told me

6:03

many years ago. But of course,

6:06

we also kind of need to

6:08

move away from it gradually in

6:10

a conversation answering your question. Obviously,

6:12

if there is no awareness of

6:14

a bias, so the bias doesn't

6:17

exist. Therefore, people just have a

6:19

feeling like everything is fine. nothing's

6:21

wrong with it. And then the

6:23

next step is how the awareness

6:25

can arrive to a person is

6:28

either the person arrives to that

6:30

awareness himself or herself or somebody

6:32

says and somebody can be a

6:34

human being and somebody can be

6:36

a technology. And when that awareness

6:38

arrives from external sources, then it's

6:41

also a question of credibility, and

6:43

there's a question of whether somebody

6:45

wants to rule my life and

6:47

suggest what's the better way of

6:49

living and all of these other

6:52

ways, how people experience, number one,

6:54

resistance, number two, denial. And those

6:56

are the typical. trajectories where people

6:58

can find themselves. So therefore, number

7:00

one awareness. It's just like with

7:03

everything, like we can look at

7:05

the addictions, for example, people are

7:07

addicted and they in denial, so

7:09

nothing will really help. Awareness number

7:11

one, and then once the awareness

7:13

is there, there has to be

7:16

a degree of willingness, willingness to

7:18

be aware. So what I usually

7:20

say, and that starts with a

7:22

young age. speaking with the kids

7:24

and then goes up to the

7:27

students and up to the adults

7:29

is do you want to that's

7:31

kind of a very trivial but

7:33

at the same time the key

7:35

essential question do you want to

7:38

do you want to be aware

7:40

do you and then after if

7:42

you want to be aware do

7:44

you want to be aware do

7:46

you and then after if you

7:48

want to be aware do you

7:51

want to do anything about it?

7:53

And from my work there is

7:55

a tool which I call circles

7:57

and people are they know what

7:59

they want and they do it

8:02

and then quite many people fall

8:04

into the middle circle which I

8:06

call the January 1st so people

8:08

kind of want to change their

8:10

lives but not always are following

8:13

up with the choices and behaviors

8:15

and decisions. Yes yeah definitely and

8:17

I had I did episode 1998

8:19

of the podcast was on the

8:21

Dunning Kruger effect which was So

8:24

I've always loved that concept myself

8:26

and it was something that ended

8:28

up being so popular beyond what

8:30

I was even expecting as people

8:32

became they got Dunning Kruger on

8:34

the Dunning Kruger effect itself in

8:37

which for anyone who hasn't listened

8:39

to that yet, that is essentially.

8:41

that competence and confidence have kind

8:43

of this strange relationship that when

8:45

you don't know things yet, when

8:48

you don't know all of what

8:50

you don't know, you're at the

8:52

peak of Mount Stupid, as is

8:54

said in kind of the fun

8:56

industry term there, where you're super

8:59

confident even though you don't know

9:01

anything about what you're talking about.

9:03

And then as you get a

9:05

little bit more competent, your confidence...

9:07

tanks and then you have to

9:09

slowly kind of work your way

9:12

back up out of the valley

9:14

of despair on there. And like

9:16

you said, that awareness point, the

9:18

knowing what you don't know is

9:20

so important. You have to then

9:23

decide, I talked about in the

9:25

episode, not everything is worth coming

9:27

up the slope of enlightenment as

9:29

they talk about in that kind

9:31

of chart there. There are some

9:34

things where you can just accept.

9:36

Wow, there's a lot more to

9:38

this than I thought and it's

9:40

not worth it. for me. So

9:42

while the red is bad in

9:45

a lot of ways that you

9:47

should maybe advance, in some cases

9:49

it's okay I think to just

9:51

sort of accept that this is

9:53

part of what I've got going

9:55

on and it's not worth my

9:58

time so I can focus on

10:00

these other things that really are

10:02

important and that I want to

10:04

change. Right. And maybe we should

10:06

give the listeners more of a

10:09

simplified vocabulary, meanwhile, kind of attaching

10:11

upon like a deep science that's

10:13

explaining how the humans change or

10:15

can't change. For sure. I think

10:17

the everyday word for that is

10:20

curiosity. I think that's kind of

10:22

we'll look at the kids there

10:24

curious. Okay, let's try this and

10:26

that we are having the same.

10:28

access to the same curiosity throughout

10:30

our lives. And this is the

10:33

question, do we allow ourselves to

10:35

be using it? Therefore, simplifying the

10:37

awareness is curiosity. Are you curious?

10:39

Are you furious that everything is

10:41

in alignment in your life? And

10:44

if a question arrives to you

10:46

that says, well, are you happy

10:48

with what you do at the

10:50

work? Are you happy with how

10:52

you spend your morning time? Are

10:55

you happy? How is your relationship

10:57

with technology? And this is how

10:59

I shifted from the industry successful

11:01

jobs, working at the Hewlett Packard

11:03

in Oracle and doing personal relationships,

11:05

having nice business cards and trips

11:08

and stuff like that into my

11:10

scientific endeavor. And it was really

11:12

about, okay, I was curious. So

11:14

if the question arrives to me

11:16

having all these kind of benefits

11:19

from the industry work, and the

11:21

question was very straightforward, then I

11:23

might assume many listeners have the

11:25

same question. Am I happy? Yes,

11:27

and I am all about questions

11:30

as the audience knows. I recently

11:32

had Warren Berger on the show

11:34

who wrote my favorite book, A

11:36

More Beautiful Questions. So for anyone

11:38

who wants to think more about

11:41

great questioning, I highly recommend that

11:43

one of his episode 200. And

11:45

so... You've talked a bit about

11:47

AI and the role of technology

11:49

and I know that is something

11:51

that people are asking about a

11:54

lot right now about how AI

11:56

and machine learning ties in with

11:58

the behavioral sciences. What are your

12:00

thoughts? Well, what are your, I

12:02

guess, simplified? for the podcast thoughts

12:05

because I know as you said

12:07

you have many many that would

12:09

take much longer than the time

12:11

you're willing to give us today.

12:13

I have a lot to say

12:16

about this topic. Let me let

12:18

me give you the kind of

12:20

key essential takeaways or key essential

12:22

perspectives. So if the question is

12:24

about the power of technology assuming

12:26

the AI is the most promising,

12:29

the artificial intelligence. So number one.

12:31

I would really encourage people to

12:33

take away unnecessary bias towards artificial

12:35

intelligence before we start the conversation

12:37

of the use of AI for

12:40

our behavioral changes. on the different

12:42

scales. So number one, I will

12:44

give you a perspective where humans

12:46

long, long time ago, our ancestors,

12:48

like many thousands of years ago,

12:51

we're trying to figure out how

12:53

we can be more intelligent as

12:55

a species. So of course, first

12:57

of all, we survive because of

12:59

the collaboration. The next was how

13:02

can we collaborate more efficiently? Okay,

13:04

let's invent speaking language. and stop.

13:06

After that, okay, it's not so

13:08

efficient we need to kind of

13:10

speak to each other, so maybe

13:12

it puts right down so everybody

13:15

can read it. Okay, that makes

13:17

life easier. The next is, oh,

13:19

I need to rewrite it all

13:21

the time, so maybe even when

13:23

it's printing, wow, nice, scaling up,

13:26

next, oh, it's accumulating in one

13:28

place, libraries, that's great. The next

13:30

thing is, oh, digital, digital, digital

13:32

is great. So let's have more

13:34

access from different locations from different

13:37

locations and we can share it.

13:39

Wonderful. And the next is the

13:41

emergence of technology, not only as

13:43

the accumulator, as the place to

13:45

share and do many other things,

13:47

but as the intelligence itself, looking

13:50

at our human intelligence, revising it,

13:52

finding a new patterns into our

13:54

knowledge, for example, or bringing pieces

13:56

together in innovative ways. And I

13:58

say that's just another contributor, intelligent

14:01

machine-driven intelligence, contributing. to coevolve in

14:03

united intelligence. That's the paradigm I

14:05

was developing in my mind and

14:07

I'm sharing this with people and

14:09

I'm like, okay, that makes sense

14:12

of number one, we are together

14:14

with it on our journey and

14:16

the only obstacles are of human

14:18

biases or of human dark sides

14:20

leveraging the tools against ourselves or

14:22

against the other human beings. Now

14:25

moving from that understanding that artificial

14:27

intelligence, this technology driven technology that

14:29

helps humans to achieve their goals.

14:31

Then we can look at behavioral

14:33

science, specifically for this podcast. Here,

14:36

a wonderful use for the AI.

14:38

AI, looking at data and trying

14:40

and giving more deeper insights into

14:42

behavioral patterns. We can start with

14:44

organizational processes, how people make decisions

14:47

when and how to do with

14:49

their technology. Everything is in a

14:51

log files. You can look at

14:53

that. So everything is registered. From

14:55

there, they can make better analysis.

14:58

visual representations and also suggestions for

15:00

decision making. So those are the

15:02

most common uses of the AI

15:04

for addressing human behavior change at

15:06

different scales. Yeah. Do you have

15:08

any projects or anything you're able

15:11

to share to kind of show

15:13

practically how you've used this? I

15:15

know that many things cannot be

15:17

shared. So if you don't have

15:19

anything, totally understand. There are there

15:22

are success stories and one of

15:24

the famous ones are addressing the

15:26

famous example is where real life

15:28

problem in organizations and in many

15:30

other occasions where people have agreed

15:33

to come and meet and discuss

15:35

and agree on certain things like

15:37

a project meetings and organizational meetings.

15:39

And we know, I think everyone

15:41

on this podcast who's listening has

15:43

been a situation when somebody arrives

15:46

late, late to the meeting. And

15:48

that was a real case, really

15:50

implementation. We simply. Simplifying to test

15:52

the proof of concept works. There

15:54

was a computer in the meeting

15:57

room, there was a bigger, let's

15:59

say TV or the screen, which

16:01

usually the conference rooms have a

16:03

TV where you can present the

16:05

PowerPoint and stuff. And that was

16:08

all connected in a sense that

16:10

when every person arrived to the

16:12

meeting, whether you were on time

16:14

or late, was giving you the

16:16

color of the meeting for the

16:18

representation of your performance on that

16:21

visual. on the screen. So if

16:23

you were on time, you've got

16:25

the color of that meeting, if

16:27

you were late, you didn't get

16:29

the color of that meeting, and

16:32

everyone was able to see their

16:34

own performance across the last meetings,

16:36

and then you could also see

16:38

how others are performing. So again,

16:40

staying away from the conventional carrots

16:43

and sticks, punishments and rewards, it

16:45

was purely based on the social

16:47

influence, how we inherently are reacting

16:49

to other people. In this case,

16:51

technology did only one thing. made

16:54

the whole process transparent and accumulated

16:56

over time and you will be

16:58

able to see who is getting

17:00

more colors in their bars and

17:02

who are not and lacking behind.

17:04

After five meetings everyone was on

17:07

time, every single person was on

17:09

time, it took five meetings and

17:11

of course we can imagine if

17:13

that design strategy was to grow

17:15

those bars depending on the next

17:18

meetings and you were either getting

17:20

more colors in your bar or

17:22

you are not, after the three,

17:24

four, or five meetings, you see,

17:26

well, there are people having like

17:29

five colors and you have none.

17:31

So you don't want that and

17:33

you want to escape that. So

17:35

that was one of the most

17:37

vivid ways. And at the same

17:39

time addressing one of the most

17:42

common human biases that people would

17:44

be just saying, oh, it's not

17:46

that important to be on time.

17:48

Or it's pointless to be on

17:50

time. I will have other other

17:53

meetings more important. I have to

17:55

take this call. I have to

17:57

answer this email and it goes

17:59

on and on. I love this

18:01

example. as you know, and when

18:04

we talked about it, it's been

18:06

a few months now since we

18:08

first talked about it and I

18:10

said, oh, that one's going in

18:12

the book, right? So my second

18:15

book is on what your employees

18:17

need and can't tell you. It's

18:19

all about change management. And I

18:21

love this example of how the

18:23

social proof makes it to where

18:25

you feel that pressure to be

18:28

on time. Really fascinating about this

18:30

example, I think, is everyone can

18:32

see if you're on time or

18:34

not. Everybody knows anyway. So why

18:36

does the chart have any impact

18:39

at all, right? Everybody should know

18:41

that I was late for the

18:43

last four meetings. So why do

18:45

I care about the chart? Why

18:47

does seeing it? make a difference.

18:50

And for everyone, for when the

18:52

book comes out, Agnes was kind

18:54

enough to provide some imagery that

18:56

should be included in there, so

18:58

you can actually see, you know,

19:00

how that looks. What sort of

19:03

insight do you have into kind

19:05

of that aspect of, it is

19:07

such a funny quirk, but we

19:09

can all feel that too, right,

19:11

where you would look at that

19:14

chart and say, oh man, I

19:16

hope my company never does something

19:18

like that. But why, right? It's

19:20

so strange. is to face our

19:22

own human nature at its core

19:25

or as its depth. And that's

19:27

the most interesting, at least for

19:29

me. Many people would like to

19:31

deny and not go that way

19:33

at all, but I'm different. That's

19:35

my most desirable. Okay, how can

19:38

I understand how the things work

19:40

deep down in our neurology, physiology,

19:42

sociology, and how it can be

19:44

defined with technology design? So that

19:46

these technologies actually make the transformation

19:49

work in a long term. So

19:51

here it is, there are two

19:53

ways how to look at this.

19:55

And let me start with the

19:57

one that would be possible. popping

20:00

into the minds of the listeners.

20:02

Oh, I don't want this. I don't

20:04

want to see myself. I don't

20:06

want to see myself in comparison

20:08

with others on the same screen.

20:11

I don't want that. You see,

20:13

that's the denial and the resistance

20:15

speaking. What they usually put up

20:17

as an argument, they say, that's

20:19

public shaming. That's the most

20:22

common way how the people would

20:24

be reacting in my talks or

20:26

in the massive classes. There is

20:28

always somebody. bringing up this

20:30

idea. And I say, well, let's take

20:32

a closer look to this. What's the

20:35

role of technology in

20:37

this experience? Similarly, as you

20:39

said, if you sit in a

20:41

meeting room without this solution, you

20:43

would be able to see who

20:45

is coming late. And if you want,

20:48

you can. write it down on your

20:50

paper. And it's kind of the basic

20:52

idea of the same thing, like a

20:54

paper-based solution. And now it's just more

20:56

digitized because we have technologies that can

20:59

do it for us. By the way,

21:01

the next thing is AI with a

21:03

camera seeing who is coming in and

21:05

doing it all for us. So that's

21:08

how it's accelerating or amplifying the effect.

21:10

So number one, should take away the

21:12

argument of public shaming is such a

21:14

response where technology is just

21:17

doing the same thing. that we

21:19

already can do is just do

21:21

it more efficiently. So we don't

21:23

need to kind of pay attention

21:25

that much. And why it feels

21:28

so different is because our brains

21:30

are very efficient at filtering what's

21:32

useful and what's not. So therefore,

21:35

if you have seen a person not

21:37

being on time or being late for

21:39

the last meeting, but that was maybe

21:42

an occasion, it's not so valuable information.

21:44

So it's erased. And that repeats for

21:46

quite some time and there is a

21:49

person always being like, of course, that's

21:51

kind of valuable information that we start

21:53

to remember who is the person or

21:56

who are the people coming alive. So

21:58

therefore technology is taking away. Number

22:00

one, the bias, because everyone can

22:02

remember, okay, that person was later,

22:05

that person wasn't, but it's inaccurate.

22:07

Our brains are good, but not

22:09

perfect. The technology, number one, is

22:11

having a more reliable perspective on

22:14

the behavioral performances of the people,

22:16

and secondly, it makes existing. patterns

22:18

just more transparent and more visible

22:20

so that we as collective look,

22:23

look at the representation and we

22:25

are not stuck into debating our

22:27

subjective perspectives. So those are the

22:29

major reasons. One on one hand,

22:32

what people are resisting and kind

22:34

of expressing their not willingness to

22:36

have them. On the other hand,

22:38

what are the real benefits and

22:41

what are the real improvements that

22:43

the companies can get by implementing

22:45

these solutions taking away these human

22:47

and driven biases. Yeah, definitely. Thank

22:50

you so much for digging in

22:52

on that. Like I said, it's

22:54

it's one of my favorite examples.

22:56

Another of my favorite examples that

22:59

I talk about all the time

23:01

when I it's kind of a

23:03

key one when I talk about

23:05

change is the literary, which I

23:07

love and you were pretty heavily

23:10

involved in that I don't think

23:12

project is the right thing to

23:14

say company the that movement. Yeah.

23:16

What can you share about and

23:19

I'll link of course there's an

23:21

episode where I talk to Michael

23:23

the CEO about the literary in

23:25

episode 75 which I know because

23:28

I talk about it all the

23:30

time like I said and I'm

23:32

always sharing about it. What role.

23:34

Did you play, what can you

23:37

share about the literary project? Number

23:39

one, the literary is the concept

23:41

and startup and idea and the

23:43

business model. How our societies can

23:46

get more... cleaner and our governance

23:48

for the local municipality more efficient

23:50

in taking care of our surroundings

23:52

to be nuts crowded with garbage

23:55

or waste around the basic promise

23:57

that also Michael says is before

23:59

there are humans on earth and

24:01

there are no litter. So it's

24:04

a man made behavioral problem and

24:06

this is all starts there. What

24:08

attracted me to this idea was

24:10

Regardless of how brilliant technology can

24:13

be developed, so they smart litter

24:15

bins, recognizing what kind of litter

24:17

it is, plastic, metal, glass, etc.

24:19

And giving a lottery ticket, if

24:22

you have sorted and put your

24:24

property in a property litter bin,

24:26

that's all fine. The angle that

24:28

I was really passionate to add

24:31

to this project was, well, yes,

24:33

you are using and leveraging the

24:35

lottery idea, which is gaining something

24:37

and anticipating that you might be

24:40

the winner. That's kind of our

24:42

optimism bias a little bit. It's

24:44

all fine. And we know lotteries

24:46

have been around for 4,000 years.

24:49

And they work in a specific

24:51

way. And I said, well, it's

24:53

great. Let's do it. to the

24:55

next level where not only the

24:58

people are engaged and attracted by

25:00

the lottery concept alone, let's add

25:02

the social influence to that. Let's

25:04

add the perspective of other people.

25:07

For example, if you have a

25:09

neighborhood and a few neighborhoods in

25:11

the city, and then depends how

25:13

well the people are sorting their

25:16

litter and putting their right bins,

25:18

we could quantify that. And this

25:20

is what... the work that I

25:22

did at the MIT Media and

25:25

the quantifying communities, then we could

25:27

be able to compare these communities

25:29

on the screens. next to the

25:31

bins. What the bins themselves would

25:34

be telling you whether you performed

25:36

well or not, depending what you

25:38

put in, but also what was

25:40

the behavior of previous people that

25:43

were throwing something into this bin.

25:45

And a bin would be having

25:47

this endless, not really analyzed, but

25:49

like continuous. continuous feedback loop about

25:52

the behavior of your choice about

25:54

the other people around this bin

25:56

and then around the neighborhood and

25:58

then comparing to other neighborhoods and

26:01

this is the way how we

26:03

are amplifying the effects of success

26:05

for that kind of solution just

26:07

by adding this social layer to

26:10

it because otherwise people if they

26:12

are left alone okay they are

26:14

just sitting and hoping okay am

26:16

I going to win or not.

26:19

But then you see, it's not

26:21

only about winning or losing, it's

26:23

about we, we as a collective,

26:25

do we want to live in

26:28

the kind of cleaner community? Do

26:30

we have the engagement? People desire

26:32

to change many things in their

26:34

lives, not only individual but on

26:37

the collective level. What have we

26:39

done wrong? Maybe not wrong, but

26:41

kind of what is our current

26:43

architecture in the cities? It's not

26:46

transparent. But technologies comes in and

26:48

technologies come and help us to

26:50

make it again more transparent like

26:52

a 2,000 years ago. Looking out

26:55

to the fields, you can see

26:57

some people are hunting there, some

26:59

people are being ground, some people

27:01

are doing something else. Our cities

27:04

now are regaining and gaining back

27:06

that and giving the possibility for

27:08

people to benefit from seeing others,

27:10

especially the good performers. That's that's

27:13

always important. Yeah, I really like

27:15

that. We haven't talked about that

27:17

before so thank you for sharing

27:19

that and it's very reminiscent I

27:22

guess or I like how it

27:24

goes kind of beyond just social

27:26

proof to so Chaldini you know

27:28

he's got his now seven principles

27:31

of persuasion and was on the

27:33

podcast talking about unity that unity

27:35

principle and I think that that's

27:37

really present there in. the being

27:40

part of something bigger and we're

27:42

all on one team working together

27:44

and being able to help reinforce

27:46

the community and something so simple,

27:49

but that can have really lasting

27:51

and compounding effects for the better

27:53

by just a tiny little ad

27:55

that can really change everything, which

27:58

is what it's all about, right?

28:00

Unfortunately, we humans. over our evolution.

28:02

We have gained a lot and

28:04

we have lost some essential awareness

28:07

of who we are, and therefore

28:09

also we have this debate about

28:11

AI being different from us, which

28:13

is wrong in many aspects, and

28:16

also the way that they against

28:18

us, seeing the differences and all

28:20

the characteristics of the people, that

28:22

is driven by one of the

28:25

social influence principles, social comparison. We

28:27

compare everything every time, depends on

28:29

the cultures that could be more

28:31

of a stronger effect of social

28:34

comparison, some of the cultures maybe

28:36

less. Generally what it means, it's

28:38

if you look back to how

28:40

you thought about the grades in

28:43

your high school, you look at

28:45

how well did you perform comparing

28:47

to your classmates and then you

28:49

were specifically looking, oh. I never

28:52

knew I would perform less than

28:54

this other person and never expected

28:56

him to perform that. That's social

28:58

comparison. It has multiple angles. So

29:01

here, by the way how our

29:03

societies have evolved, and of course

29:05

huge impact this by technological advancements,

29:07

radio, television, mass communication media, social

29:10

media and so forth, and it's

29:12

again depends how it was designed

29:14

and how it was used again

29:16

by other human beings to shift

29:19

our society and our society of

29:21

thinking. But there's a good news.

29:23

We are coming back to our

29:25

basis of our human nature. more

29:28

and more see that people want

29:30

to choose healthier lifestyles. They want

29:32

to live in a safer communities.

29:34

They want to get together so

29:37

that they improve their quality of

29:39

life. And not only as individuals

29:41

in society, but also as entrepreneurs,

29:43

as a company, as the products

29:46

and services, and all this paycheck

29:48

to the earth, idea on the

29:50

balance sheet, okay, what is the

29:52

impact? So therefore, I see a

29:55

good trends. While the dark side

29:57

of our human nature, I'm not

29:59

saying we should get rid of

30:01

it. I think we cannot. A

30:04

DNA is just remembering all the

30:06

dark ages that we went through

30:08

as a human nature. But now,

30:10

thanks to technology, we get more

30:13

deeper and clearer, more detailed perspective

30:15

of who we are. And if

30:17

we use our intellectual capacity to

30:19

say out to ourselves, well, the

30:22

dark sides comes from the dark

30:24

ages and we would like to

30:26

leave them there and we will

30:28

use the positive and the beneficial

30:31

ones for today and we will

30:33

leverage technologies for our own benefit

30:35

collectively and not for making any

30:37

unnecessary tensions between groups of people

30:39

or countries or perspectives and so

30:42

forth. So I see it's and

30:44

also thanks to the scholars like

30:46

Shelvini and others who have done

30:48

great contribution to helping people to

30:51

realize how the things work on

30:53

this intercommunication level and then also

30:55

giving that as giving that as

30:57

inside full tools for technology designers.

31:00

So Chilvini is one, BJ Fogg,

31:02

and there are many other scholars

31:04

and scientists who have done a

31:06

great help, especially remembering the BJ

31:09

Fogg from Stanford University. He runs

31:11

his behavioral design lab right now,

31:13

and he wrote the book, Persuasive

31:15

Technology almost 20 years ago, and

31:18

I think that was really, really

31:20

good books for the whole community

31:22

that currently is. Yes, technology can

31:24

help us. Not only we facilitate

31:27

each other, technology can be designed

31:29

for behavioral positive behavior. with changes.

31:31

Yeah, and when you were talking,

31:33

I was realizing, remembering, thinking about,

31:36

so I did a series really

31:38

early on on the podcast that

31:40

I called All the Biuses, and

31:42

I broke them into different categories,

31:45

like the way we think about

31:47

ourselves, the way we think about

31:49

others, things to do with memory,

31:51

just kind of putting them into

31:54

some sort of categories to help

31:56

make sense of many, many biases

31:58

there. through all more than 200

32:00

episodes now, and even though we've

32:03

talked about AI and things like

32:05

that, haven't really had anyone talk

32:07

much about the research and what

32:09

people have found about our biases

32:12

toward technology. And knowing, like, we

32:14

know that a, you know, a

32:16

coworker is a machine versus a

32:18

person or if we're playing games

32:21

against a computer versus a person,

32:23

how we feel differently about that.

32:25

And where we have this really

32:27

interesting. connection to technology, I guess,

32:30

where it's this very polarized relationships.

32:32

We really love it until we

32:34

don't. And then we really, really

32:36

hate and fear it. Can you

32:39

give any insight into some of

32:41

that for people that haven't read

32:43

any of that research or heard

32:45

much about it yet? I think

32:48

it's fascinating. It is. It definitely

32:50

has. Technologies have huge impacts and

32:52

it is such a multifaceted and

32:54

multi-dimensional experience ever since the first

32:57

initial digital technology. Before that we

32:59

had a regular mechanical technology and

33:01

we have mix of technical, electrical,

33:03

all the software, all these technologies

33:06

are now impacting the way how

33:08

we experience. The bias towards technology,

33:10

I wouldn't, there is, there is

33:12

such a thing. That's for sure.

33:15

That's how we experience it. And

33:17

I agree with the kind of

33:19

the ways how new this crime

33:21

so we start with okay we

33:24

look for hope and help and

33:26

making our life. comfortable and enjoy

33:28

that until we find something that

33:30

we don't like about it. Maybe

33:33

it's a question of reliability or

33:35

maybe that's a high expectations we

33:37

have that this technology should deliver

33:39

and work 100% of the time.

33:42

Nevertheless, it's important to realize that

33:44

most likely, and I will make

33:46

a hypothesis here because that's not

33:48

the core of my work, but

33:51

this is a very neighboring area

33:53

so I can I can give

33:55

a educated guess for what we

33:57

scientists call hypothesis. Technology is perceived

34:00

by an average human being, the

34:02

same way as an average human

34:04

being, perceives another human being. So

34:06

we look for trust, and we

34:09

call reliability. We look for consistency,

34:11

which means the level of accessibility

34:13

technology. Then we look for long-term

34:15

relationship. Okay, how many times did

34:18

we have good experience with it

34:20

and how many times there was

34:22

a breakup with technology? So all

34:24

of these and more and much

34:27

more is coming through, I would

34:29

even dare to say to the

34:31

to the way how we are

34:33

trying to impose our human characteristics

34:36

and values on any non-human objects.

34:38

poor experiences with the non-human events.

34:40

The same way how we see

34:42

a face on the moon and

34:45

the same way, and we see

34:47

cats in the clouds. And why?

34:49

Because it was essential for our

34:51

survival. If we walk through the

34:54

forest many thousand years ago, we

34:56

better kind of read the other

34:58

human beings and the faces. Are

35:00

they friendly or not? Are they

35:03

telling us the truth or not?

35:05

The same we expect from technology.

35:07

And that's why. It's not always

35:09

trivial and especially when we think

35:12

about humanized robots. Yes, we would

35:14

on the one hand see similarity

35:16

with the other human beings, and

35:18

then would expect, for example, emotional

35:21

intelligence. But they are still working

35:23

on that. And when they first,

35:25

the robot makes the first mistake,

35:27

we disqualify it. And the takeaway

35:30

here is, technology is a tool,

35:32

we are developing it, our intentions

35:34

are behind, whether it's good or

35:36

bad, ethical or unethical, so. Everything

35:39

we experience with technology is giving

35:41

us opportunity to look at ourselves.

35:43

And the more advanced technology we

35:45

develop, the closer and deeper we

35:48

see ourselves in the whole spectrum

35:50

of our bad and good, bright

35:52

and dark sides of the human

35:54

nature. And that's going to continue.

35:57

And if you have this awareness,

35:59

then we also are aware of

36:01

the biases towards technology because they...

36:03

pretty much should be the same

36:06

biases we have against other people,

36:08

against other nations, against different other

36:10

things that we would naturally do

36:12

without technology. So it should be

36:15

the same game, just applied to

36:17

another actor, which is technology driven.

36:19

Awesome. Thank you for taking that.

36:21

little sort of side path with

36:24

me, I appreciate it. And I

36:26

would be remiss if we did

36:28

not talk about the Skype method

36:30

for hyperperformance before you go because,

36:33

you know, it has your name.

36:35

So you should share a little

36:37

bit about what it is and

36:39

how you use that to help

36:42

people and companies. For a long

36:44

time I was labeling it as

36:46

a transformation design methodology on a

36:48

different other words that were attached

36:51

as it was emerging from a

36:53

deep scientific understanding of the human

36:55

change and then refining how the

36:57

changes are different and then getting

37:00

to a way to investigating where

37:02

is the problem and then applying

37:04

technology so that technology can help

37:06

us to deal with that problem

37:09

more consistent way. So that was

37:11

the journey. It all started by

37:13

my question to my question. myself,

37:15

which I mentioned earlier in the

37:18

podcast, am I happy? And I

37:20

said, no, I'm not really. So

37:22

I went to invest again. What

37:24

is the possibility to merge, to

37:27

intertwine, to blend technology intelligence with

37:29

the human intelligence for our own

37:31

benefit? And this is how over

37:33

the years, I arrived to Stephen

37:36

Method. And Stephen Method implies, and

37:38

there are three major stages. There

37:40

are 10 tools, and they are

37:42

splitting the three stages. First two

37:45

tools fall into the guidance phase.

37:47

So we kind of look for

37:49

what is the best way for

37:51

us to help ourselves and the

37:54

guidance tools have to not only

37:56

specify where we want to get,

37:58

which I call the green vector,

38:00

and at the same time it

38:03

helps to map out the ways

38:05

that we don't want to go.

38:07

By the way, in the management,

38:09

for example, oftentimes people have or

38:11

the companies have vision and mission.

38:14

But do they have the map

38:16

around that area where they get

38:18

the traps of the previous habits

38:20

and people leading to the previous

38:23

poor decisions that they've done before

38:25

until they don't have these other

38:27

traps or the map of the

38:29

traps? They just will fall into

38:32

them without even knowing that. So

38:34

therefore, the first step is guidance

38:36

and there are two tools. One

38:38

is the pathology of change, understanding

38:41

between the one time period of

38:43

time and the long term changes

38:45

which are. transaction, transition, transformation, then

38:47

is a vector. Okay, there's a

38:50

green vector. There should be yellow

38:52

vectors and the red vectors and

38:54

then mapping out what are the

38:56

likelihood you are actually constantly sticking

38:59

with your desired direction. That means

39:01

the green vector. And after that

39:03

starts the framework, the framework of

39:05

eight tools and there are actually

39:08

there are three phases. The first

39:10

is investigation phase with four tools.

39:12

Most of the times the problem

39:14

with failed solutions are that there

39:17

is not enough awareness where really

39:19

is the problem. So therefore four

39:21

tools go to investing. investigate, investigate,

39:23

and find out that the most

39:26

of the problems reside in human

39:28

thinking. Most of the biggest challenges

39:30

for performance, and especially for hyper

39:32

performance, are the human biases, poor

39:35

decisions, conscious, productive thinking, and all

39:37

of those sorts of things. Once

39:39

that's realized, they can start designing

39:41

and using technology. Then we have

39:44

design phase. Design phase fundamentally is

39:46

data driven with. intelligence to analyze

39:48

this data. And then, which is

39:50

essential, which is the top layer

39:53

of the architecture, which comes after

39:55

the big buzz around smart cities

39:57

and so forth, is a transforming

39:59

layer. How we communicate this information,

40:02

relevant information back to our primary

40:04

target audience, stand users. And this

40:06

is where I really emphasize and

40:08

I cannot emphasize more instant feedback

40:11

loops. Instant feedback loops means if

40:13

you receive your electricity bill next

40:15

month. It doesn't really have any

40:17

instant reflection or it doesn't give

40:20

you the time to do that.

40:22

But if you have, every single

40:24

time you switch on the lab

40:26

at home or you switch it

40:29

off, you see something like a

40:31

colors of the light, the end

40:33

of the light changes, it gives

40:35

you the instant feedback. That's essential

40:38

for designing. And the other component

40:40

is social influence. It means we

40:42

integrate into that instant feedback group

40:44

about other positive behaviors, people in

40:47

your building, switching of the previous

40:49

month. that encourages you to follow

40:51

or get the inspiration. And the

40:53

final phase, the crucial phase is

40:56

to make like assurance for a

40:58

long-term success. And there are two

41:00

tools. One is how you can

41:02

avoid the times when everybody is

41:05

just switching all the lights on

41:07

and just burning down the energy.

41:09

And that would naturally, the social

41:11

influence is working with the same

41:14

power both ends. And you don't

41:16

want your system to promote the

41:18

opposite behavior just for the... I

41:20

know because there is a celebration

41:23

going on or something like this.

41:25

So that's one and the final

41:27

one is ethics. So we need

41:29

to be very aware that it's

41:32

not that. that is willing to

41:34

do bad. It's how we are

41:36

on one hand, sometimes having bad

41:38

intentions. Some people might have bad

41:41

intentions using the same tool. And

41:43

the other one is something from

41:45

our psychology. We can predict many

41:47

of behavioral aspects, but there are

41:50

some unpredictability. So we cannot predict

41:52

that there will be some emergence

41:54

of the subgroup of the people

41:56

would say, well, oh, this is

41:59

how you do it. Do you

42:01

light up the buildings during the

42:03

night? the residents of the building

42:05

was comparing to the previous week.

42:08

This is how you do it.

42:10

So I will rebel and I

42:12

will switch all my life just

42:14

to kind of not allow you

42:17

to look up my building and

42:19

I will destroy our building's performance

42:21

just because I don't like the

42:23

solution to have employment. So that's

42:26

that's shortly the overview of the

42:28

method. Awesome. And I know where

42:30

you're talking about some of the

42:32

vectors and aspects and color things

42:35

there. We will be linking to

42:37

some articles and to your website

42:39

that has more information for people

42:41

that you are now intrigued and

42:44

you want to go take a

42:46

look and really see what he's

42:48

talking about will definitely have the

42:50

link there because as with anything

42:53

those visuals do help us to

42:55

kind of get a feel for

42:57

what's there and clearly you know

42:59

like you said there are 10

43:02

aspects there there's a lot going

43:04

on in this model it's very

43:06

very useful and helpful to have

43:08

that sort of cheat sheet that

43:11

you can go to for sure.

43:13

So as I said, definitely linking

43:15

to that. And for everyone that

43:17

is now so excited to go

43:20

learn more about what you do

43:22

and connect with you, watch your

43:24

TED Talk, TED Talks, I'm not,

43:26

I'm not sure, this multiple, I

43:29

believe. Yes. Where are the best

43:31

places to go to connect and

43:33

learn more about you? So if

43:35

you are the person. who is

43:38

more in the preference of entertainments,

43:40

you welcome to my. YouTube channel.

43:42

There are like short videos explaining

43:44

some of the things and some

43:47

are more personal development related, some

43:49

of the organizational, some explain the

43:51

method. So that's kind of a

43:53

place. I think it's very appropriate

43:56

for the times that many people

43:58

are learning through watching the videos.

44:00

And again, what's good about it?

44:02

Videos are like usually containing other

44:05

person telling you, which is a

44:07

social influence and it's at its

44:09

core. So that's one, but if

44:11

you are more serious. If you

44:14

really want to dig deeper, then

44:16

you're welcome to my website, which

44:18

is my name, surname, dot com.

44:20

And then you have the scientific

44:23

literature behind that, you have the

44:25

cases, success stories, you can have

44:27

some interesting collections of the videos

44:29

that I have. If you don't

44:32

want to just browse the YouTube,

44:34

you can have like a more

44:36

specifically tailored way of the contents

44:38

related with my work. Wonderful, and

44:41

you know, right there for anyone

44:43

who is excited to have Agnes

44:45

come do a talk for your

44:47

company or, you know, you want

44:50

to have more information, you know,

44:52

there are links there for more

44:54

information, you can, you know, schedule

44:56

a call to talk with him

44:59

about coming in to speak to

45:01

your group, whatever it is that

45:03

you're looking for, I say, definitely

45:05

check it out and there will

45:08

be links in those show notes

45:10

waiting for you. Thank you again,

45:12

Dr. Seib for joining me on

45:14

the show. I always learned something

45:17

chatting with you and I'm sure

45:19

everyone listening learned lots of things

45:21

today as well. My pleasure. Always

45:23

welcome. See you next time. Sounds

45:26

good. So, what got your brain

45:28

buzzing as you learned from Agnes

45:30

today? For me, while the literary

45:32

is a big piece of what

45:35

I always associate with him, I

45:37

also love the example of social

45:39

proof to help get people to

45:41

show up on time to meetings.

45:43

This little somewhat counterintuitive nudge is

45:46

so powerful and a great example

45:48

of reframing a problem to change

45:50

behavior. It is so often our

45:52

tendency to be too myopic when

45:55

looking to solve a problem. You

45:57

look right at what you think

45:59

the problem is and jump into

46:01

trying to solve it instead of

46:04

stepping back and asking thoughtful questions

46:06

to see what might be hiding

46:08

on the periphery. What's something we

46:10

could do just before or adjacent

46:13

to the problem that we're seeing

46:15

now that might have more influence

46:17

on the behavior we're trying to

46:19

change? Those questions can lead to

46:22

such amazing insights and true behavior

46:24

change, which is fantastic. And of

46:26

course Agnes's work in achieving hyper

46:28

performance is why I refreshed this

46:31

episode today. It felt like the

46:33

perfect primer for the conversation that's

46:35

airing in just a couple days

46:37

with Barry Conchy and Sarah Dalton

46:40

discussing the five talents that really

46:42

matter. How great leaders drive extraordinary

46:44

performance. Now that your brain is

46:46

working on this idea of hyperperformance

46:49

at work, you will be set

46:51

up to understand these five key

46:53

talents that matter, which Barry and

46:55

Sarah evaluated over 58,000 executives to

46:58

unlock and share. It's super fascinating

47:00

stuff and I know you don't

47:02

want to miss that episode. If

47:04

you aren't already subscribed to the

47:07

brainy business podcast, now is a

47:09

great time to do so to

47:11

ensure you don't miss that or

47:13

any other episode. As we close

47:16

out the show, don't forget about

47:18

those show notes with links to

47:20

my top related past episodes and

47:22

books and more. It's all waiting

47:25

for you in the app you're

47:27

listening to and at the brainy

47:29

business.com/472. And thank

47:32

you again to Dr. Agnes Steep

47:34

for joining me on the show

47:36

today. It was a delight to

47:38

chat with and learn from you.

47:40

Join me Friday for a brand

47:42

new episode with Barry Conchy and

47:44

Sarah Dalton, co-authors of the five

47:46

talents that really matter. It's going

47:48

to be a lot of fun.

47:50

You don't want to miss it.

47:52

Until then, thanks again for listening

47:54

and learning with me. And remember

47:56

to be thoughtful. Thank

48:00

you for listening to the

48:02

Braney Business podcast. Molina offers

48:05

virtual strategy sessions, workshops, and

48:07

other services to help businesses

48:09

be more brain-friendly. For more

48:11

free resources, visit the Braney

48:14

Business.com.