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Image credit: BiteEdge, from “Prediction markets are about to be a big deal”

1 – “GOOD Thinking”: The Beginning of a Project Plan

Below is the beginning of a development and launch plan for the “GOOD IDEAS about Our Future” podcast. 

Purpose

Launch a podcast about how current and emerging technologies can be used to build healthier, more prosperous communities. 

Outcomes

  • Provide various points of view on how emerging technologies are affecting our lives every day at a high level that’s accessible to general audiences
  • Highlight recent research on the opportunities and pitfalls of cutting-edge technologies
  • Share clear calls to action for how to leverage technology and advocate for policy changes so new tools can be used to improve lives and communities 

Process 

Audience

Although the podcast will remain accessible to the general public, it will include recommendations for how listeners, technology professionals and academics can dig deeper (i.e., “go down rabbit holes”) to learn more and have conversations within their networks. 

Cover Art

A simple, engaging image will need to be developed to promote the podcast on various websites. 

Recommended Length and Frequency

30 minutes the first and third Tuesday each month. Why? A person’s attention span is, on average, less than 20 minutes. Savvy podcasters will break up episodes with intentional halftime sessions of under 60 seconds, but it’s still extremely ambitious to set the expectation for hour-long content without a large audience. It’s better to start at 30 minutes, set the expectation there, and increase or decrease length based on what data show after a few months.

Structure – General

Pre-scripted material that summarizes key concepts and incorporates relevant stories/anecdotes to increase engagement and accessibility of concepts. 

Equipment and Development

  • Audio-Technica ATR2100X bundle – includes all needed hardware for high(er) quality recording
  • “Free Radical” licensing – for mid- and end-roll music
  • Ecamm Call Recorder – for Skype interview recording
  • QuickTime – for recording audio
  • Podcast template creation (10 hours) — covers pre-, mid-, and end-roll creation, identifying adjustments for audio profile, and setting up workflow
  • Research and script writing (6 hours)
  • Recording and editing (2 hours)

2 – Intrade.com Mess

According to independent journalist and blogger Gwern Branwen:

Emphasis is added on the most important characteristic of a prediction market, the way in which it differs from regular stock markets. The idea is that by tracking accuracy—punishing ignorance & rewarding knowledge in equal measure—a prediction market can elicit one’s true beliefs, and avoid the failure mode of predictions as pundit’s bloviation or wishful thinking or signaling alignment.

However, according to The New Yorker‘s “What Killed Prediction Markets,” author John Cassidy explains that InTrade was neither accurate or even transparent (or perhaps ethical) about its financial structure: “If [InTrade] wasn’t very deep or very liquid, then there were some legitimate questions about whether it could manipulated by somebody willing to expend not very much money.” So in other words, InTrade was essentially a Ponzi scheme disguised behind free market theory that eventually fell apart as the deceptions and fraudulent dealings it was built upon came to light following the death of Founder and CEO John Delaney, according to Philip Bump in The Atlantic article “Questions Surround Millions InTrade Paid CEO at Time of His Everest Death.”

With the Irish Central article in mind, I would say that “prediction markets” is just techno-speak for digital gambling, which has a long, sordid history (the histories of Las Vegas, Chicago, and even Hot Springs come to mind). Just as bringing gambling into the digital space expedites and can even increase the size of bets, it also speeds up opportunities for money laundering, fraud, and all sorts of unsavory financial activities that regulatory systems exist to prevent (or at least reduce).

3 – The Delphi Method and Prediction Contracts

While the Delphi Method allows for a broad range of questions related to “how” and “when” something will happen, prediction market contracts require a boolean outcomes. So, while I could ask, “When will dinosaurs be cloned?” as a solid Delphi Method question, a prediction market contract would have to be framed with a greater element of certainty connected to a True/False outcome, such as betting that “The next Jurassic Park-franchise movie, Jurassic World: Dominion will exceed box office sales for the last film, Jurassic World: Fallen Kingdom, by more than 25 percent.” Note that there is a change from asking a question to issuing a statement that must be proven to satisfy the conditions of the contract.

 

4 – Sample Prediction Contracts

(Not a valid contract — for educational use only)

Contract #XX
 
Prediction Market Contract
 
This document hereby certifies that 
 
The broker (Big Big Predictions Company, philanthropy’s most famous prediction marketplace)

will pay the last owner 
and charge the first owner 
of this contract
the sum of 
 
—-ONE HUNDRED DOLLARS ($100) IN CASH—-
 
if the following event occurs as specified:

Arkansas will pass a bill to adopt a state-based earned income tax credit
for any reason and any period of time between
6:00 a.m. CST on Monday, January 11, 2021
and
11:59 p.m. CDT on Friday April 30, 2021
 
Otherwise this contract shall have a value of $0.00 (nothing)
 
 
Sold by________________ to_____________for $______ on _________
 
Sold by________________ to_____________for $______ on _________
 
Sold by________________ to_____________for $______ on _________
 
Sold by________________ to_____________for $______ on _________
 
Sold by________________ to_____________for $______ on _________
 
Sold by________________ to_____________for $______ on _________
 
Sold by________________ to_____________for $______ on _________
 
Sold by________________ to_____________for $______ on _________

5 – I Watched “Really Achieving Your Childhood Dreams”

A – Recommendations for Achieving Your Childhood Dream

  1. Help others: I have to agree with him here. If your sole focus is yourself, you can achieve very little (and perhaps your dreams are a bit small). By helping others, they are more likely to reciprocate. Additionally, helping others helps us learn, teaches us incredible lessons about ourselves, and lends clarity to what we hope for for ourselves, our families, and our communities.
  2. Decide if you’re a Tigger or an Eeyore: Life is full of painful and challenging experiences just as it’s full of incredible opportunities. Taking the time to own our agency, especially when faced with chaos and uncertainty can actually turn challenges into opportunities to grow, learn, and later celebrate. And although clinical depression and other mental illnesses can make seeing opportunity within adversity more difficult, all people have the ability to frame how they perceive what they face. Per my favorite line in Milton’s Paradise Lost, which comes from Satan: “The mind is its own place and in itself, can make a Heaven of Hell, a Hell of Heaven.”
  3. Never give up: Again, I agree here. And while there is plenty of research on the power of hope and a sense of agency during difficult and even life-threatening times, I want to draw attention to research that hope is one of the strongest treatments for cancer patients — note that I say “treatment” and not “cure” because it improves quality of life throughout the experience with cancer. As Pausch points out, achieving anything worthwhile requires grit, tenacity, and hope that what you set out to do can and will be achieved.

B – Aspirational Philosophy in Life

Personally, I’d say aspiring for my childhood dreams is ludicrous. Not getting too far into the details here, but my childhood wasn’t great, and my greatest aspiration was to escape what I’ll only describe as a less than desirable environment. For someone like Pausch, who clearly had loving and supporting parents, he was equipped with the agency and support to dream big about his future. For someone like me, I have developed a very different aspiration: To live life intentionally, radically accept what happens both good and bad, and aspire to maintain a baseline of contentment. I’ve developed this approach after several years of therapy. And I constantly seek new ways of re-thinking and challenging how I go about “living with intent.” For example, there’s a great podcast that I find helpful as I go on life’s journey called “The Good Life Project,” which recognizes and embraces this approach. This philosophy works for me because it allows me to acknowledge pain and trauma that is an inevitable part of life — greater for some more than others — while permitting me the freedom to reflect on, process, and learn from experiences through intentional decisions and a practice of remaining present in every moment.

6 – More from Weapons of Math Destruction

In this latest installment, O’Neil describes the hidden ways our backgrounds of privilege and oppression play out in questionnaires and documents that shape our lives. For example, the Level of Service Inventory – Revised (LSI-R) is a tool used to determine the level of sentencing convicted individuals receive. Affluent, white respondents have had life experiences they write in that would suggest they’re not a threat; however, economically insecure individuals and people of color are documented as being a “greater risk” simply because systemic barriers to success and greater scrutiny by law enforcement is a part of their everyday lives.

O’Neil goes on to explain that the predictive models that determine everything from loan application decisions to prison sentencing are not only inherently biased, the biases that produce systems of oppression remained unquestioned and invisible because they are developed “scientifically,” but the process of development adheres to the first no-no anyone learns in STATS 101 — correlation is not causation. No one is challenging why these “scientific” instruments are resulting in disproportionately rejecting job applicants who are people of color, maintaining poverty in low-income neighborhoods, and incarcerating Black men with longer prison terms.

Lastly, because data acquisition methods and corresponding procedures for decision-making are considered empirically grounded, they have transferability. As O’Neil says:

And here’s one more thing about algorithms: they can leap from one field to the next, and they often do. Research in epidemiology can hold insights for box office predictions; spam filters are being retooled to identify the AIDS virus. This is true of WMDs as well. So if mathematical models in prisons appear to succeed at their job—which really boils down to efficient management of people—they could spread into the rest of the economy along with the other WMDs, leaving us as collateral damage. (p. 31)

 So rather than actually doing the work (e.g., assessing job candidates based on level of ability and job experience), they look at the result of one predictive model’s result (e.g., a lengthy prison sentence). In other words, because the information economy has spead everything up and connected various systems, employers, marketers, policymakers, and other entities take shortcuts because the false belief is maintained that a “scientific” instrument is infallable. 

As for criticisms of this book, reviewers were correct in saying O’Neil takes shortcuts of her own in presenting her argument. However, the robust citations make deeper, more thorough research easily accessible to the reader. Especially for someone like me who has chosen to read this book on my Kindle.