"Humans obviously evolved a much wider range of communication tools to express their thoughts, the most important being language," said Hoffecker, a fellow at CU's Institute of Arctic and Alpine Research. "Individual human brains within social groups became integrated into a neurologic Internet of sorts, giving birth to the mind."

"Crowd algorithms often assume workers are inexperienced and thus fail to adapt as workers in the crowd learn a task. These assumptions fundamentally limit the types of tasks that systems based on such algorithms can handle. This paper explores how the crowd learns and remembers over time in the context of human computation, and how more realistic assumptions of worker experience may be used when designing new systems. We first demonstrate that the crowd can recall information over time and discuss possible implications of crowd memory in the design of crowd algorithms. We then explore crowd learning during a continuous control task. Recent systems are able to disguise dynamic groups of workers as crowd agents to support continuous tasks, but have not yet considered how such agents are able to learn over time. We show, using a real-time gaming setting, that crowd agents can learn over time, and `remember' by passing strategies from one generation of workers to the next, despite high turnover rates in the workers comprising them. We conclude with a discussion of future research directions for crowd memory and learning."

"We consider the problem of designing the the utility functions of the utility-maximizing agents in a multi-agent system so that they work synergistically to maximize a global utility. The particular problem domain we explore is the control of network routing by placing agents on all the routers in the network. Conventional approaches to this task have the agents all use the Ideal Shortest Path routing Algorithm (ISPA). We demonstrate that in many cases, due to the side-effects of one agent's actions on another agent's performance, having agents use ISPA's is suboptimal as far as global aggregate cost is concerned, even when they are only used to route infinitesimally small amounts of traffic. The utility functions of the individual agents are not "aligned" with the global utility, intuitively speaking. As a particular example of this we present an instance of Braess' paradox in which adding new links to a network whose agents all use the ISPA results in a decrease in overall throughput. We also demonstrate that load-balancing, in which the agents' decisions are collectively made to optimize the global cost incurred by all traffic currently being routed, is suboptimal as far as global cost averaged across time is concerned. This is also due to 'side-effects', in this case of current routing decision on future traffic. The mathematics of Collective Intelligence (COIN) is concerned precisely with the issue of avoiding such deleterious side-effects in multi-agent systems, both over time and space. We present key concepts from that mathematics and use them to derive an algorithm whose ideal version should have better performance than that of having all agents use the ISPA, even in the infinitesimal limit. We present experiments verifying this, and also showing that a machine-learning-based version of this COIN algorithm in which costs are only imprecisely estimated via empirical means (a version potentially applicable in the real world) also outperforms the ISPA, despite having access to less information than does the ISPA. In particular, this COIN algorithm almost always avoids Braess' paradox."

This paper argues that democracy can be seen as a way to channel “democratic reason,” or the collective political intelligence of the many. The paper hypothesizes that two main democratic mechanisms - the practice of inclusive deliberation (in its direct and indirect versions) and the institution of majority rule with universal suffrage - combine their epistemic properties to maximize the chances that the group pick the “better” political answer within a given context and a set of values. The paper further argues that under the conditions of a liberal society, characterized among other things by sufficient cognitive diversity, these two mechanisms give democracy an epistemic edge over versions of the rule of the few.

"It is still an open question when groups will perform better than individuals in intellectual tasks. We report that in a company takeover experiment, groups placed better bids than individuals and substantially reduced the winner’s curse. This improvement was mostly due to peer pressure over the minority opinion and to group learning. Learning took place from interacting and negotiating consensus with others, not simply from observing their bids. When there was disagreement within a group, what prevailed was not the best proposal but the one of the majority. Groups underperformed with respect to a “truth wins” benchmark although they outperformed individuals deciding in isolation. "

"The Millennium Project was founded in 1996 after a three-year feasibility study with the United Nations University, Smithsonian Institution, Futures Group International, and the American Council for the UNU. It is now an independent non-profit global participatory futures research think tank of futurists, scholars, business planners, and policy makers who work for international organizations, governments, corporations, NGOs, and universities. The Millennium Project manages a coherent and cumulative process that collects and assesses judgments from over 2,500 people since the beginning of the project selected by its 40 Nodes around the world. The work is distilled in its annual "State of the Future", "Futures Research Methodology" series, and special studies."

"Remember when I said in our last post that our problems are no longer technological? What I meant was that developing the technologies we need to save our collective asses is no longer the big issue

"Why is it even possible to have misunderstandings online when we have all these tools at hand to help prevent them? It's because social media systems like Facebook are just the tricycle version of what social media will become. Facebook barely hints at what's coming

"Here's my take on things: our biggest challenges are no longer technological. They are issues of communication, coordination, and cooperation. These are, for the most part, well-studied problems that are not wicked. The methodologies that solve them need to be scaled up from the small-group settings where they currently work well, and injected into the DNA of our society--or, at least, built into our default modes of using the internet. They then can be used to tackle the wicked problems."

"I would also add --- and this is something Henry and I have ben thinking about a lot --- that it is often not at all trivial to figure out what your interests are, or how to achieve them, and that (small-d) democrats should try to find ways to help people work that out. Actually having political clout is often going to depend on collective action, but this needs to be complemented by collective cognition, which is how people figure out what to want and how to achieve it. That, however, is part of a much larger and rather different story, for another time. "

"Ledface has a very specific goal: to enable people to tap into collective intelligence to acquire information to solve their day-to-day problems. Think of it as a new kind of social network in which people interact with each other indirectly, through knowledge, through Ledface.
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At Ledface, everyone can ask and we will match the question with the group best suited to answer. We select a group of people who match each query and we ask them to interact in real time as a team to create the answer. They share their thoughts, combine them, and review each other’s input. So you don’t get a list of replies, but a specific, custom answer co-created in real time each time you ask. No names, no ego, just knowledge."