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Printable Handouts
Navigable Slide Index
- Introduction
- New technologies meets human skillsets
- The problem
- My main theme today
- My secondary themes:
- “Change comes whole”
- Do not rely on model output
- The financial modelers manifesto
- Holistic implications: 1/1.1
- "All models are wrong"
- Each model's output is only one view
- Holistic implications: 2
- Models typically fail when needed the most
- Model failure is a reliable crisis timing tool
- Holistic implication: 4
- Holistic implication: 4.1
- Assessment of failure
- Holistic implication: 5
- Holistic implication: 5.1
- Reference
This material is restricted to subscribers.
Topics Covered
- Holistic thinking
- Improving holistic judgment
- Over-reliance on model outputs
- Failure of models as a tool
Talk Citation
Werther, G. (2018, July 1). The machine-human interface: how to better judge emerging events [Video file]. In The Business & Management Collection, Henry Stewart Talks. Retrieved December 21, 2024, from https://doi.org/10.69645/JTYN9509.Export Citation (RIS)
Publication History
Transcript
Please wait while the transcript is being prepared...
0:00
My name is Guntram Werther,
I'm Professor of Integrated Business Applications Group
at the Fox School of Business, Temple University.
Today's talk is the "Machine-Human Interface.
How to Better Judge Emerging Events".
0:12
We have wonderful new technology.
It allows us to do all kinds of new things
and we're still working with the same human skill sets.
I think this has happened several times in
human history and what we're talking about today eventually
is going to require that we build up those human
skill sets to interact more effectively with that new technology.
And I'll try to show that to you with some data I have and some other comments.
0:35
So, the problem is improving machines with the same old human judgment.
I have some data from the US and Canada, primarily.
The first is average integrative thinking ability of
experienced intelligence analysts we're looking at
15 years or better is about a 1.5 on a five-scale.
This is from the Director of National Intelligence, United States.
For industry, also again,
15 years of ability for the average analyst.
These are very experienced analysts in the world's largest companies.
They self-rate the ability to take machine and arithmetic outputs to
create effective real world judgments at about a two to three out of five-scale.
And then if we ask them what happens in a rare event or crisis event, again,
the average ability to take that machine/arithmetic output and create
effective real world judgments falls to about a one or two on a five-scale.
Now, these are self-judgments,
they're done informally so as not to embarrass anybody.
But I'm pretty sure if you do this in Europe and other parts of the world,
you're going to come up with roughly parallel information.
Certainly, you're welcome to do that on your own but
that's my finding over the last several years.
1:36
My main theme today is that the biggest real world assessment challenge in emerging
future's foresight challenge in
the early 21st century is building up better holistic human judgment.
My comments are limited to human-involved systems.
In other words, I'm not going to talk about earthquake prediction or
global warming or hurricane prediction or something that I know absolutely nothing about.
I'm focused on human-involved systems;
in other words, social systems,
political systems, cultural systems.
Conversation, one dealt with this integrative thinking ability at that level.
This conversation focuses more explicitly on
the human-machine interface and how to get better judgment at that interface.