Blockchains deploy Transaction Fee Mechanisms (TFMs) to determine which user transactions to include in blocks and determine their payments (i.e., transaction fees). Increasing demand and scarce block resources have led to high user transaction fees. As these blockchains are a public resource, it may be preferable to reduce these transaction fees. To this end, we introduce Transaction Fee Redistribution Mechanisms (TFRMs) -- redistributing VCG payments collected from such TFM as rebates to minimize transaction fees. Classic redistribution mechanisms (RMs) achieve this while ensuring Allocative Efficiency (AE) and User Incentive Compatibility (UIC). Our first result shows the non-triviality of applying RM in TFMs. More concretely, we prove that it is impossible to reduce transaction fees when (i) transactions that are not confirmed do not receive rebates and (ii) the miner can strategically manipulate the mechanism. Driven by this, we propose \emph{Robust} TFRM (\textsf{R-TFRM}): a mechanism that compromises on an honest miner's individual rationality to guarantee strictly positive rebates to the users. We then introduce \emph{robust} and \emph{rational} TFRM (\textsf{R}$^2$\textsf{-TFRM}) that uses trusted on-chain randomness that additionally guarantees miner's individual rationality (in expectation) and strictly positive rebates. Our results show that TFRMs provide a promising new direction for reducing transaction fees in public blockchains.

In a semi-realistic market simulator, independent reinforcement learning algorithms may facilitate market makers to maintain wide spreads even without communication. This unexpected outcome challenges the current antitrust law framework. We study the effectiveness of maker-taker fee models in preventing cooperation via algorithms. After modeling market making as a repeated general-sum game, we experimentally show that the relation between net transaction costs and maker rebates is not necessarily monotone. Besides an upper bound on taker fees, we may also need a lower bound on maker rebates to destabilize the cooperation. We also consider the taker-maker model and the effects of mid-price volatility, inventory risk, and the number of agents.

It's been ten years since Nest first launched its first smart thermostat, and it's become the most popular brand in the connected home temperature control space. If you've seen one of its products, you'll recognize the distinctive puck-like shape and rotating edge controls. Now that it's ten years old, though, it's time for the thermostat to get a glow up. Google is launching the new Nest Thermostat today for $130, and it features an impressively sleek, attractive makeover that'll make the device look less like a bump on your wall and more like an elegant ornament. It's available in four colors --snow, sand, charcoal, and fog and looks significantly smaller than before.

It's a great time to upgrade if you're looking to score a new graphics card for cheap. With Nvidia's high-end GeForce RTX 2070 and GeForce RTX 2080 and 2080 Ti now available, we're starting to see some seriously enticing discounts on graphics cards for lower budgets too--a merciful situation after a year of cryptocurrency-induced price inflation. If you're looking to level up to no-compromises 1080p gaming or push a 1440p monitor screaming past 60fps, we've found deals worth checking out. The most enticing deals come in around the $200 price range, where you'll find graphics cards that deliver superb 1080p gaming experiences. First up: The PowerColor Red Dragon Radeon RX 570, which is on sale for just $160 at NeweggRemove non-product link. This card should deliver a strong 60 frames per second with all or most in-game graphics settings cranked, though as always, that varies game by game.

Sometimes you find a graphics card deal that's just too good to pass up. Today's that day: Newegg is selling the 8GB MSI RX 570 Armor OC for $160Remove non-product link. As with most Newegg graphics card deals, you'll need to remember to apply for a $30 rebate, but it's still one of the best prices we've ever seen for an 8GB RX 570 card. In fact, it's just $10 more expensive (after the rebate) than a $150 4GB RX 570 deal we recommended earlier in October. Today's deal ends on Wednesday, October 24.

This package includes stand-alone Python code to run any of the included/available Relief-Based algorithms (RBAs) designed for feature weighting/selection as part of a machine learning pipeline (supervised learning). Presently this includes the following core RBAs: ReliefF, SURF, SURF*, and MultiSURF*. Additionally, an implementation of the iterative TuRF mechanism is included. It is still under active development and we encourage you to check back on this repository regularly for updates. These algorithms offer a computationally efficient way to perform feature selection that is sensitive to feature interactions as well as simple univariate associations, unlike most currently available filter-based feature selection methods.

I'm done with artificial intelligence. I will settle for human intelligence. Our access to human intelligence -- let's call it HI -- is increasingly limited in our online environment. Humans have become a rare species, accessed only after hours wasted waiting on the phone. Recently, I applied online for the Power Smart rebate on my new, energy-efficient heat pump that purrs away on the wall in my Saturna Island home.

Welcome to California Inc., the weekly newsletter of the L.A. Times Business Section. Traders return to business Monday after learning Friday that orders for long-lasting manufactured goods sank 6.8% in July, the biggest fall in nearly three years. Even so, manufacturers have rebounded from a slump in late 2015 and early 2016 caused by cutbacks in the energy industry and a strong dollar. Cheaper food: The impact of Amazon's purchase of Whole Foods Market will reach consumers Monday, when the high-end grocery chain begins cutting prices. Whole Foods will reduce prices on certain "bestselling staples," including bananas, salmon and organic large brown eggs.

The most prominent way to establish trust between buyers and sellers on online auction sites are reputation mechanisms. Two drawbacks of this approach are the reliance on the seller being long-lived and the susceptibility to whitewashing. In this paper, we introduce so-called escrow mechanisms that avoid these problems by installing a trusted intermediary which forwards the payment to the seller only if the buyer acknowledges that the good arrived in the promised condition. We address the incentive issues that arise and design an escrow mechanism that is incentive-compatible, efficient, interim individually rational and ex ante budget-balanced. In contrast to previous work on trust and reputation, our approach does not rely on knowing the sellers' cost functions or the distribution of buyer valuations.