LLMエージェントによる都市規模危機対応シミュレーションにおける時間的リアリズムの追求

Human collective participation is rarely steady in time: it is bursty, with short episodes of intense activity separated by long quiet intervals. In crisis response and community mobilization, predicting when people act matters as much as predicting whether they act. Such settings are increasingly modeled with LLM-based social simulators, yet these simulators are validated on whether each action is individually plausible, not on whether actions are timed as in reality. Their temporal realism, the degree to which simulated activity reproduces the bursty, heavy-tailed timing of real human systems, thus remains untested. We examine this gap using a multi-year, city-scale log of offline volunteering in Shenzhen that spans the COVID-19 pandemic. Empirically, we establish that bursty timing is common at individual and tracked-group levels, that it is largely endogenous and self-exciting, and that it is amplified by the pandemic rather than produced by daily activity cycles. A standard LLM-only simulator reproduces almost none of this timing: its synchronous schedule has no self-excitation channel, so agents act on a near-regular clock. Guided by these findings, we build a simulator in which a data-calibrated self-excitation channel and a crisis-period regime decide when each agent acts and query the LLM only at those moments, leaving it to decide which task to join and whether to commit. The LLM-only baseline yields no bursty agents (median burstiness $B=-0.14$); a single data-calibrated gate is then sufficient to lift per-agent timing above the burst threshold (median $Bapprox0.37$) without degrading LLM content decisions. These results indicate that temporal realism in LLM-based crisis-response simulation is best achieved by decoupling when agents act, governed by an explicit self-excitation and crisis-activation mechanism, from what they do, governed by the LLM.