AI 中文总结
研究订单簿市场中羊群行为与流动性，应用布沙尔相图方法于连续双拍卖模型，通过7x6网格定位流动性压力交叉，分析其稳健性及反身机制，还进行双市场分析，发现无方向性跨市场传染。
AI 中文摘要
基于主体的市场模型容易产生突发的不稳定性，但要区分真正的集体效应和参数假象需要严谨性。我们将布沙尔的相图方法应用于连续双拍卖订单簿模型。该方法是绘制完整的相图，测试其对规则变化的稳健性，并在将任何特征称为临界点之前排除退化和数值起源。该模型具有基本面锚定的零智能流动性和中间锚定的图表主义羊群层，由羊群的比例φ和强度κ控制。一个7x6网格（336次运行，每次都有一个加扰符号的零值）定位出一个突发的流动性压力交叉。序参量，即单边订单簿事件的比例，在(φ,κ)=(0.9,1.0)时上升到约0.34，在所有42个加扰单元格中为零，并形成一个平滑的交叉而不是不连续的暗角。枯竭在规则上是稳健的（在订单流不平衡规则下会再次出现），在时间范围上是稳健的（在动量窗口的16倍范围内约为0.32 - 0.35），并且有一个单调的起始边界φ*(κ) = {0.55, 0.45, 0.36}。然后我们在匹配的方向偏差幅度（平均|p_buy - 0.5|约为0.269）下分解机制。价格动量羊群行为具有一个大的、比较器稳健的反身成分（+0.29；买入引发买入），而订单流规则的成分约为0且依赖于比较器。均方根定价梯度是一个放置假象，在κ = 0时最大。一个配套的双市场分析发现在仅信号羊群链接中没有方向性的跨市场传染。
英文摘要
Agent-based models of markets readily produce emergent instabilities, but telling a genuine collective effect apart from a parameter artefact takes discipline. We apply Bouchaud's phase-diagram method to a continuous-double-auction order-book model. The method is to map the full phase diagram, test its robustness to rule changes, and rule out degenerate and numerical origins before we call any feature a tipping point. The model has fundamental-anchored zero-intelligence liquidity and a mid-anchored chartist herding layer, controlled by the fraction $φ$ and the strength $κ$ of herders. A 7x6 grid (336 runs, each with a scrambled-sign null) locates an emergent liquidity-stress crossover. The order parameter, the fraction of events with a one-sided book, rises to about 0.34 at $(φ,κ)=(0.9,1.0)$, is zero across all 42 scrambled cells, and forms a smooth crossover rather than a discontinuous Dark Corner. The dry-up is rule-robust (it recurs under an order-flow-imbalance rule), horizon-robust (about 0.32-0.35 across a 16x range of momentum window), and has a monotone onset boundary $φ^*(κ) = \{0.55, 0.45, 0.36\}$. We then decompose the mechanism at a matched directional-bias amplitude (mean |p_buy - 0.5| about 0.269). Price-momentum herding carries a large, comparator-robust reflexive component (+0.29; buying begets buying), whereas the order-flow rule's component is about 0 and comparator-dependent. The RMS-mispricing gradient is a placement artefact, largest at $κ=0$. A companion two-market analysis finds no directional cross-market contagion across a signal-only herding link.
