Semi-cooperative behaviors are intrinsic properties of human drivers and
should be considered for autonomous driving. In addition, new autonomous
planners can consider the social value orientation (SVO) of human drivers to
generate socially-compliant trajectories. Yet the overall impact on traffic
flow for this new class of planners remain to be understood. In this work, we
present study of implicit semi-cooperative driving where agents deploy a
game-theoretic version of iterative best response assuming knowledge of the
SVOs of other agents. We simulate nominal traffic flow and investigate whether
the proportion of prosocial agents on the road impact individual or system-wide
driving performance. Experiments show that the proportion of prosocial agents
has a minor impact on overall traffic flow and that benefits of
semi-cooperation disproportionally affect egoistic and high-speed drivers.

本文研究了自主驾驶中的半合作行为以及社会价值导向的影响，提出了一种基于博弈论的迭代最优响应模型进行模拟，发现半合作行为对高速和利他型驾驶者的影响更为显著，而总体交通流量的影响相对较小。

研究半合作驾驶员对公路总体流量的影响

Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow

Smart contracts are computer programs that are executed by a network of
mutually distrusting agents, without the need of an external trusted authority.
Smart contracts handle and transfer assets of considerable value (in the form
of crypto-currency like Bitcoin). Hence, it is crucial that their
implementation is bug-free. We identify the utility (or expected payoff) of
interacting with such smart contracts as the basic and canonical quantitative
property for such contracts. We present a framework for such quantitative
analysis of smart contracts. Such a formal framework poses new and novel
research challenges in programming languages, as it requires modeling of
game-theoretic aspects to analyze incentives for deviation from honest behavior
and modeling utilities which are not specified as standard temporal properties
such as safety and termination. While game-theoretic incentives have been
analyzed in the security community, their analysis has been restricted to the
very special case of stateless games. However, to analyze smart contracts,
stateful analysis is required as it must account for the different program
states of the protocol. Our main contributions are as follows: we present (i)~a
simplified programming language for smart contracts; (ii)~an automatic
translation of the programs to state-based games; (iii)~an
abstraction-refinement approach to solve such games; and (iv)~experimental
results on real-world-inspired smart contracts.

本文介绍了一种新的针对智能合约的，关于可量化属性的形式化分析框架，并提出了设计一种简化的智能合约编程语言，自动翻译程序为状态感知博弈，以及实验结果的方法。

智能合约的定量分析

Quantitative Analysis of Smart Contracts

We prove that every bounded Lipschitz function F on a subset Y of a length
space X admits a tautest extension to X, i.e., a unique Lipschitz extension u
for which Lip_U u = Lip_{boundary of U} u for all open subsets U of X that do
not intersect Y.
This was previously known only for bounded domains R^n, in which case u is
infinity harmonic, that is, a viscosity solution to Delta_infty u = 0. We also
prove the first general uniqueness results for Delta_infty u = g on bounded
subsets of R^n (when g is uniformly continuous and bounded away from zero), and
analogous results for bounded length spaces.
The proofs rely on a new game-theoretic description of u. Let u^epsilon(x) be
the value of the following two-player zero-sum game, called tug-of-war: fix
x_0=x \in X minus Y. At the kth turn, the players toss a coin and the winner
chooses an x_k with d(x_k, x_{k-1})< epsilon. The game ends when x_k is in Y,
and player one's payoff is
F(x_k) - (epsilon^2/2) sum_{i=0}^{k-1} g(x_i)
We show that the u^\epsilon converge uniformly to u as epsilon tends to zero.
Even for bounded domains in R^n, the game theoretic description of
infinity-harmonic functions yields new intuition and estimates; for instance,
we prove power law bounds for infinity-harmonic functions in the unit disk with
boundary values supported in a delta-neighborhood of a Cantor set on the unit
circle.

本文的主要研究领域包括利普希茨函数、最紧凑的扩展、无限迭代分形算子 Δ∞、博弈论和无限调和函数，其中提出了一种新的博弈理论描述来解决微分几何学和非线性分析中的一些问题。

拔河和无穷拉普拉斯方程

Tug-of-war and the infinity Laplacian

We consider a dynamic social network model in which agents play repeated
games in pairings determined by a stochastically evolving social network.
Individual agents begin to interact at random, with the interactions modeled as
games. The game payoffs determine which interactions are reinforced, and the
network structure emerges as a consequence of the dynamics of the agents'
learning behavior. We study this in a variety of game-theoretic conditions and
show that the behavior is complex and sometimes dissimilar to behavior in the
absence of structural dynamics. We argue that modeling network structure as
dynamic increases realism without rendering the problem of analysis
intractable.

本论文探讨了一个动态社交网络模型，其中代理人在随机演化的社交网络中进行重复博弈互动，并通过学习行为决定加强哪些交互关系，从而形成网络结构。作者研究了不同的博弈理论条件，显示网络结构动态复杂，有时与无结构动态时的行为不同，并认为将网络结构建模为动态可以增加现实性而不妨碍问题分析。