Collaborating On Digital Foresight Platforms
Once we’ve opened our eyes to acceleration and development, built some inclusive models and practical definitions, started to ground them in research, and created initial professional standards for certification and continuing education, we reach the threshold of a new level of credibility for our field. But we won’t cross that threshold until we embrace open, accelerating, digital tools and platforms for building collective foresight. Science and technology drive accelerating change. That means we need to be experimenting with and building digital platforms for doing foresight, to improve the value and sophistication of our field. Without these, we are stuck in the past. So let’s turn to that challenge now.
We need better ways to collaboratively find trends, build forecasts, scenarios, and predictions, critique them, and to rate source material. We’ve talked about predictive analytics, statistical forecasting, predictive intelligence, prediction markets, online Delphis and simulations, and other data-rich and collaborative approaches to foresight. Companies using these approaches will continue to grow rapidly in value.
Remember that the digital world is the fastest growing system and process on the planet right now. Leading digital companies’ technology platforms, like Google’s search servers, are growing in storage and processing capacity at 50% a year, nearly as fast as the web grows itself. In 2014, Amazon Web Services added enough new storage capacity every day to house their entire e-tailing business in 2004. Many of the other cloud infrastructure providers are growing even faster in percentage terms, and differentiating their suites with unique new developer services as well. The software platform landscape now changes so fast that information technology foresighters must continually reassess it, or their strategies quickly grow obsolete.
Examples of emerging digital platforms were introduced in Chapter 1. They include crowd forecasting platforms like Good Judgment, the science of which is outlined in Tetlock’s Superforecasting (2015). They include platforms for online Delphi, crowd consulting, and group simulation, like Wikistrat. They include emerging science and technology prediction markets, like Metaculus, and political prediction markets like PredictIt. More experimental digital foresight platforms include Augur, a prediction market company. Auguer raised over $4M in the first month of their crowdsale of REP tokens for their prediction market. Augur is built on Ethereum, a decentralized blockchain network similar to the Bitcoin network, a platform for transparent peer-to-peer contracting that many digital activists are excited about. The last well-known US prediction market was InTrade, whose operations were shut down by federal regulators in 2012. InTrade collided with the SEC and Commodities and Futures Trading Corporations, regulators who maintain monopolies, under current US law, restricting companies from betting on things like stock and commodities prices.
Whether Augur succeeds or fails as the first open money-based prediction market platform for anything people want to bet on seems too early to say. They may not get sufficient market adoption to match their development budget, in these early and still prediction-shy times, and our current legal environment, and its use by more established competitors may keep them in legal difficulties for years. But the fact that the crowd gave Augur so much money so quickly is a great sign that large numbers of investors are ready to use digital platforms for a wide variety of foresight-related activities. Augur is thus one of the new faces of Big Foresight, as we’ll outline at the end of chapter. We can expect many more of these kinds of digital, data-rich, and collectively intelligent companies ahead. This is great news for foresight practice, as it quantifies and monetizes pieces of our work in exciting new ways, and recruits many more of us into online foresight creation activities.
Another key to the future of foresight collaboration will be more platforms that rate, share, and critique foresight work, and the individuals and institutions that do that work and training. We need increasingly open, software-aided, and cognitively diverse criticism in our field, to find the weeble stories, preference landscapes, and counterfactuals, and to help each of us see more what we are still missing.
We also need better ways to assess the skills and reputations of foresight professionals and their institutions. LinkedIn is making small steps in this direction with its skills testimonials, and the ability to display online course certifications and other assessments. Klout offers an early tool for assessing social media influence. But the real work of online reputation and crowdrating has barely begun. See Michael Fertik’s The Reputation Economy, 2015, for a tour of how much we can now easily learn about each other using online tools today, and some fascinating thoughts on where digital reputation may go in coming years.
The promise of foresight for humanity will only be realized when our work becomes broadly socially recognized and respected as one of ways we make a better world. If accelerating change continues, at some point the world will wake up to see what we’ve been saying, and we’ll value foresight as much as we value the past and the present today, rebalancing us in time. In the meantime, we need to do all we can to help our field become better respected, connected, trained, and paid.
Personal foresight needs to be taken seriously as a major part of coaching, counseling, and self-development. Organizational foresight across the skills and specialties needs to be valued like any other function of management. Foresight needs to be something every leader feels naked without. Global foresight needs to help all of us planetary citizens wake up to the realities of accelerating change, a wide range of developmental trends and destinations, and the incredible opportunities those trends offer us, if we only look.
For individuals, organizations, and societies, there is always a limit to how much adaptive foresight, and what balance of foresight skills and specialties are useful in any context. Pursue any foresight type, skill, specialty, or method too much and you will waste resources and dissipate momentum. But ignore a sufficient number of them, and you will be flying blind and unprotected.
At present, most of us are usually in a state of too little foresight work. We are often unaware which foresight skills and functions we are ignoring, and how we need to change our mix to be more adaptive. By developing more evidence-based models, collaborating more closely, harnessing accelerating technology, and using constructive critique, we can and will do better for our planet, our clients, our families, and ourselves.
