Institutions play a critical role in enabling communities to manage common-pool resources and avert tragedies of the commons. However, a fundamental issue arises: Individuals typically perceive participation as advantageous only after an institution is established, creating a paradox: How can institutions form if no one will join before a critical mass exists? We term this conundrum the institution bootstrapping problem and propose that misperception, specifically, agents' erroneous belief that an institution already exists, could resolve this paradox. By integrating well-documented psychological phenomena, including cognitive biases, probability distortion, and perceptual noise, into a game-theoretic framework, we demonstrate how these factors collectively mitigate the bootstrapping problem. Notably, unbiased perceptual noise (e.g., noise arising from agents' heterogeneous physical or social contexts) drastically reduces the critical mass of cooperators required for institutional emergence. This effect intensifies with greater diversity of perceptions. We explain this counter-intuitive result through asymmetric boundary conditions: proportional underestimation of low-probability sanctions produces distinct outcomes compared to equivalent overestimation. Furthermore, the type of perceptual distortion, proportional versus absolute, yields qualitatively different evolutionary pathways. These findings challenge conventional assumptions about rationality in institutional design, highlighting how "noisy" cognition can paradoxically enhance cooperation. Finally, we contextualize these insights within broader discussions of multi-agent system design and collective action. Our analysis underscores the importance of incorporating human-like cognitive constraints, not just idealized rationality, into models of institutional emergence and resilience.

It was more than even the most ardent advocates expected. After all the demonstrations of superhuman prowess, and the debates over whether the technology was humanity's best invention yet or its surest route to self-destruction, artificial intelligence landed a Nobel prize this week. And then it landed another. First came the physics prize. The American John Hopfield and the British-Canadian Geoffrey Hinton won for foundational work on artificial neural networks, the computational architecture that underpins modern AI such as ChatGPT.

It is likely that 2023 will be remembered as the year of Artificial Intelligence (AI). ChatGPT [2] was the fastest internet service to reach 100million users until now (May 2023) [3] and the technology of Large Language Models (LLMs) at its core is a fundamental element of sister apps for images such as Dall-e2, Midjourney and many others. One of the most fascinating aspects of LLMs is that they exhibit unpredicted emergent features. While the media excitedly reported how AI art generator have developed their own taste [4] or chatbots are able to pass school level exams in a growing number of disciplines [5], only in 2023 it was released that, for the past two years, GPT models had consistently improved its performance in tests designed to measure theory of mind in children [6]. For anyone familiar with complexity science, observing emergent properties in a complex system made of billions of artificial neurons is perhaps not surprising, but the growth in human-, or even superhuman-, like capabilities has attracted huge attention from the media and the public, sparking a hectic debate between the technology apocalyptic and integrated [7]. While it is clear that AI could bring us spectacular benefits, from better medical diagnosing to drug discovering, the risks have so far catalysed most of the public attention. Perils associated to narrow AI include increasing opportunities for manipulation of people, enhancing and dehumanising weapons, and rendering human labour increasingly obsolescent [8]. On the other hand, selfimproving "artificial general intelligence" (AGI) could pose an existential threat to humanity itself.

We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms. We propose a simple theoretical model of a collective interacting with a firm's learning algorithm. The collective pools the data of participating individuals and executes an algorithmic strategy by instructing participants how to modify their own data to achieve a collective goal. We investigate the consequences of this model in three fundamental learning-theoretic settings: the case of a nonparametric optimal learning algorithm, a parametric risk minimizer, and gradient-based optimization. In each setting, we come up with coordinated algorithmic strategies and characterize natural success criteria as a function of the collective's size. Complementing our theory, we conduct systematic experiments on a skill classification task involving tens of thousands of resumes from a gig platform for freelancers. Through more than two thousand model training runs of a BERT-like language model, we see a striking correspondence emerge between our empirical observations and the predictions made by our theory. Taken together, our theory and experiments broadly support the conclusion that algorithmic collectives of exceedingly small fractional size can exert significant control over a platform's learning algorithm.

Artificial intelligence has hit the mainstream. Across industries, companies have rolled out successful proofs-of-concept and have even been successful in deploying AI in production. Some organizations have even operationalized their AI and machine learning strategies, with projects proliferating across the enterprise, complete with best practices and pipelines. Today, companies at the leading edge of the AI maturity curve are making use of AI at scale. This overall maturation of how AI is deployed in enterprises is shifting how companies view the strategic value of AI -- and where they hope to see its benefits realized. Here is a look at 10 AI enterprise strategy trends that industry experts are seeing unfolding today.

Artificial intelligence has hit the mainstream. Across industries, companies have rolled out successful proofs-of-concept and have even been successful in deploying AI in production. Some organizations have even operationalized their AI and machine learning strategies, with projects proliferating across the enterprise, complete with best practices and pipelines. Today, companies at the leading edge of the AI maturity curve are making use of AI at scale. This overall maturation of how AI is deployed in enterprises is shifting how companies view the strategic value of AI -- and where they hope to see its benefits realized. Here is a look at 10 AI enterprise strategy trends that industry experts are seeing unfolding today.

And I am no committed die-hard Marxist either. In this paper I am merely asking if our planning, evaluations & reviews of investments made in education, employment and human capital from tax payers' money over the years till now (especially since 1991) been judicious enough to warrant comfort in future outputs. Inviting my readers to do a self (mental) due diligence of achievements and the progress made in our country in the past few decades as I do, all I am asking is if, given the commitments radiating among our warring political parties under an archaic political system, the future of our grandchildren safe enough? Or, given they will not join the emerging lumpen elements, ought we to plan their migration to as bizarre countries as Taiwan, China, South Korea?] "Bureaucracy served Man well in the past. But the nature of Work has changed and management must change for us to survive. Our goal is to move from a bureaucratic model that is focused on maximizing compliance to one that is focused on maximizing contribution"– Management Guru Gary Hamel, speaking on Humanocracy at an Open Interactive pop up on 18th February 2021.

Let me be upfront: I was the technical co-founder of an AI startup and it failed. PharmaForesight was an AI startup in the pharmaceutical business intelligence industry. "The rate of return for pharmaceutical companies on their R&D is currently below their cost of capital -- therefore it is becoming less profitable for pharmaceutical companies to invest in innovative drugs. To decide what clinical trials to conduct, the likelihood of approval is a crucial metric which is currently being calculated in a very subjective and biased way. Our AI algorithm can estimate this figure much more accurately, saving time, money and ultimately benefits patients."

Although for many organizations artificial intelligence may be a work in progress, its rapid spread across the enterprise has led Gartner to predict that by 2024, 75% of organizations will shift from piloting to operationalizing artificial intelligence (AI). This in turn will drive an increase in streaming data and analytics infrastructures by up to 500%. It is likely that the current health crisis has sped up the rate of deployment. During the pandemic and with millions working from home, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) have been able to provide insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures. It has also enabled many organizations to continue doing business where they might otherwise have had to slow down or even shut down.

Artificial intelligence is one of the most powerful technologies for reshaping business in decades. It has the ability to optimize many processes throughout organizations and is already the engine behind some of the world's most valuable platform businesses. In our view AI will become a permanent aspect of the business landscape and AI capabilities need to be sustainable over time in order to develop and support potential new business models and capabilities. Specifically, we believe that companies need to establish dedicated organizational units to entrench AI. This is an important business tool that cannot be left to bottom-up whimsy.