The distinction is more than philosophical hair-splitting. In explicit-time models, chronological information is fed into the simulation from the outside, usually as georeferenced radiocarbon dates calibrated against the calendar. Time acts as an independent variable, much as it does in Newtonian mechanics, where the letter t enters equations of motion as a given axis along which everything else unfolds. In emergent-time models, by contrast, no calendar is imposed at the start. Instead, temporality arises endogenously from repeated cycles of local interaction, adaptive feedback and cumulative transformation, and only afterward does the researcher assign a time scale to the model’s internal steps so that its outputs can be compared with dated evidence. The authors argue that this difference has profound implications for how computational models of social dynamics can be tested against the historical record.

To give the argument formal teeth, the team proposes a deceptively simple unit of analysis: the event, defined as a bounded, identifiable occurrence that marks a discernible difference in the state of a system. Formally, each event is a tuple binding an agent, an action, a location and a time—e = (agent, action, location, time). This structure, the authors contend, can remain invariant across wildly different kinds of evidence. In textual history, all four slots are filled directly: named individuals, described deeds, toponyms and calendar dates. In archaeology, the tuple must be reconstructed inferentially—agents are anonymous, actions are read from material traces, locations are known with centimeter precision, and time is probabilistic rather than calendrical. In depositional events, the raw sedimentary record, the agent slot may be filled by gravity, water or frost rather than any human intention at all.

As one moves from texts to artifacts to sediments, the authors describe a gradient of epistemic directness: agency dissolves from named persons to impersonal forces, action shifts from described intention to measured process, location gains precision while losing meaning, and time loses resolution while gaining depth. This is precisely where the distinction between time as a parameter and temporal information becomes critical. A list of radiocarbon dates provides time-as-parameter—numbers without relational structure. A model of early farming dispersal extracts temporal information by asking how those dates relate to one another across space, what patterns of proximity and sequence they form, and what processes could have generated the observed distribution.

The Mediterranean Neolithic serves as the paper’s empirical proving ground. The spread of farming from the Levant to the Iberian Peninsula between roughly 7000 and 5500 BC is one of the best-documented large-scale social transitions in prehistory, supported by thousands of georeferenced radiocarbon dates. The region’s geographical heterogeneity—mountains, seas and ecological gradients—produced strikingly uneven spread rates, with rapid coastal expansion standing out against slower inland diffusion. This makes the Mediterranean an ideal benchmark for comparing how different temporal representations shape our understanding of prehistoric process.

Equation-based models, the first of the three simulation families analyzed, treat time as explicitly parametric. Reaction-diffusion systems, descended from the classical wave-of-advance framework of Ammerman and Cavalli-Sforza, combine a demographic reaction term—population growth—with a diffusion term describing spatial spread. The authors draw an analogy with Newton’s second law: demographic pressure, climate change, contact with farming groups and social incentives act as forces pushing a population toward agriculture, while the society’s cultural resistance functions as an inertial mass that must be overcome. The velocity term represents the rate at which farming spreads, and its derivative captures whether adoption is accelerating or decelerating. Crucially, the integrated equation shows that the current rate of spread is not determined solely by present conditions but by the entire accumulated history of driving forces—a genuinely historical property.

In these models, empirical dates enter at only two moments: to set the starting conditions and to validate the output. Between those points, time is purely parametric. But this apparent limitation becomes a strength when the framework is inverted: because the arrival dates at specific locations are known, the model’s free parameters—growth rate, dispersal distance, and the balance between demic migration and cultural transmission—are constrained by the data. Only parameter combinations that reproduce the observed chronology remain admissible. The authors note that the cultural transmission parameter can only be broadly bounded from chronological data alone, and that genetic clines were needed to pin down its actual value, illustrating how different strands of evidence complement the temporal constraint.

Agent-based models operate in almost the opposite mode. Here, autonomous computational agents—individual farmers, households or entire settlements—follow simple decision rules within a shared virtual environment, planting crops, fallowing land, relocating or exchanging goods based on local conditions. Time is not a pre-existing axis but an organizing framework for discrete interactions: each timestep is one iteration of an economic cycle in which agents perceive, decide, act and update their state. The historical tempo that emerges—pulses, pauses, leapfrogs—comes from internal feedbacks such as resource depletion, density dependence and learning, not from any external database. Only after the simulation completes does the researcher assign calendar years to the iteration steps, converting the model’s internal ordering into comparable chronological information. The result is a dataset of emergent adoption events, each tying a who, a what, a where and a when.

Network-based models offer a third path, representing history as temporal event graphs in which nodes are events, agents or entities and edges encode dependencies. In timestamped versions, each node carries a duration and edges can be given birth and decay times, allowing the network to be grown as a sequence of snapshots. In a more radical variant, no time parameters are supplied at all: distance-weighted diffusion on a static spatial graph generates emergent timestamps from iterative message-passing, with nodes activating stochastically based on local history. Time steps are calculated from interactions rather than introduced as external input, producing a stream of relational events with internally generated temporal order.

The authors conclude that the two approaches are complementary rather than competing. Emergent-time simulations excel at reconstructing how processes may have unfolded mechanistically, illuminating contingency, feedback and path dependence. Explicit-time analyses excel at confronting hypotheses with empirical chronological evidence, ruling out scenarios that fail basic chronological tests and constraining equifinality. Their combined use encourages a cyclical research strategy in which exploratory emergent models generate hypotheses, explicit analyses evaluate them against dated evidence, and discrepancies prompt refinement. By expressing both as variations of a single event tuple, the framework provides a common ground for integrating heterogeneous simulation approaches—turning time from an invisible backdrop into a measurable, testable dimension of historical explanation.

Subject of Research: Temporal representation in computer simulations of historical social processes, including the spread of early farming in the prehistoric Mediterranean

Article Title: Time as event: bridging explicit and emergent temporal representations in computer-based social simulations

Article References: Time as event: bridging explicit and emergent temporal representations in computer-based social simulations. (n.d.). https://doi.org/10.3389/fcpxs.2026.1838587

Image Credits: AI Generated

DOI: 10.3389/fcpxs.2026.1838587

Keywords: computational archaeology, agent-based modeling, reaction-diffusion, Neolithic transition, temporal representation, simulation, Mediterranean prehistory, radiocarbon dating, event-based analysis, social simulation, emergent time, historical dynamics

The study’s central premise is deceptively simple. Movement across terrain is never uniform. A valley floor invites travel; a steep ridge resists it. When archaeologists map ancient settlements, they typically note where sites are located, but the new work argues that the more revealing question is how easily each location could be reached from surrounding areas. This concept, known as topographic accessibility, can be modeled using cost-distance analysis, a technique borrowed from geography and transportation studies that calculates the cumulative effort of moving across a digital elevation model. Rather than measuring straight-line distance, cost-distance methods weight every step of a journey by the friction imposed by slope, elevation change, and other terrain features, producing a surface that shows how connected—or isolated—each point on the landscape would have been for people traveling on foot.

Applied to the Neolithic landscapes of China, this approach transforms a scatter of known archaeological sites into a structured network of accessible and inaccessible places. The researchers compiled settlement data spanning key phases of the Chinese Neolithic, a period stretching roughly from the sixth to the second millennium BCE, during which millet-farming communities in the north and rice-farming communities in the south gradually intensified production, expanded their footprints, and began clustering into settlements of unprecedented size. Sites associated with cultures such as Yangshao and Longshan in the Yellow River region, and contemporaneous developments in the Yangtze basin, show a well-documented trajectory from small hamlets to large nucleated villages, some surrounded by ditches or earthen walls, with evidence of craft specialization, social hierarchy, and coordinated labor.

The analysis reveals that accessibility was not a passive backdrop to this trajectory but an active structuring force. Settlements that occupied highly accessible locations—places where multiple low-cost routes converged—tended to sit at nodes in a developing settlement hierarchy, positioned to receive people, goods, and information flowing across the landscape. Less accessible sites, by contrast, were more likely to remain small and peripheral. This pattern echoes central place theory, a foundational idea in geography holding that settlements of different sizes arrange themselves in predictable patterns relative to the populations they serve. What makes the new findings striking is that they suggest proto-urban organization in Neolithic China emerged in dialogue with terrain long before markets, administrative institutions, or written records existed to coordinate such arrangements.

The technical machinery behind the study deserves attention, because it illustrates how computational archaeology is reshaping the field. Digital elevation models, often derived from satellite remote sensing, provide continuous terrain surfaces at resolutions fine enough to capture the ridges, terraces, and river valleys that shaped prehistoric movement. From these surfaces, researchers can generate least-cost path networks—hypothetical routes that minimize travel effort between pairs of locations—and can aggregate those routes into accessibility surfaces that quantify each location’s reachability. Graph-based representations then treat settlements as nodes connected by these modeled pathways, allowing measures such as centrality, connectivity, and network efficiency to be computed for each site. When these measures are compared against independent archaeological indicators of settlement importance, such as site area, the presence of fortifications, or the density of associated smaller sites, correlations between accessibility and proto-urban prominence become statistically testable rather than merely impressionistic.

Such methods do not come without caveats, and the study is careful to acknowledge them. Least-cost models assume that prehistoric travelers behaved like rational cost-minimizers, when in reality movement is shaped by social obligations, ritual pathways, seasonal flooding, vegetation, and the distribution of resources that no elevation model can capture. The resolution of archaeological survey data is another constraint: regions differ in how intensively they have been walked, excavated, and recorded, which can bias apparent settlement patterns. The researchers address these issues through sensitivity testing, comparing results across alternative cost functions and spatial scales to confirm that the accessibility–settlement relationship is robust rather than an artifact of modeling choices. This kind of methodological transparency is increasingly expected in computational archaeology, where a striking map can easily outrun the evidence beneath it.

Why does this matter for the broader story of urban origins? The conventional narrative of urbanism, built largely on Mesopotamian evidence, emphasizes irrigation agriculture, surplus storage, temple institutions, and coercive political power as the engines that concentrated people into cities. Neolithic China offers a partially independent case, one in which large nucleated settlements arose in a different ecological and agricultural context, based on millet and rice rather than wheat and barley, and in a landscape of loess plateaus, river terraces, and monsoon-fed plains. If topographic accessibility systematically shaped settlement organization there, it suggests that some principles of proto-urban patterning are general: wherever populations grow and interact, the geometry of the terrain helps decide which places become hubs and which remain hinterlands. Terrain, in other words, may be one of the oldest pieces of urban infrastructure.

The findings also speak to a lively debate about what proto-urban even means. Some archaeologists reserve the term for settlements that exhibit clear functional differentiation—storage facilities, workshops, elite residences—while others apply it more loosely to any settlement large enough to require coordination beyond the kin group. The accessibility analysis adds a spatial criterion to this discussion: proto-urban settlements are not merely big, they are positioned. They occupy locations that maximize reach within a regional movement network, which in turn would have facilitated the exchange of food, raw materials, marriage partners, and information that large populations require. Position and function thus reinforce each other, and the study’s quantitative framework allows researchers to evaluate that reinforcement across hundreds of sites rather than a handful of celebrated examples.

There are implications here for how archaeologists interpret specific regions of Neolithic China. The loess plateau of the middle Yellow River, with its deeply incised valleys and flat-topped terraces, presents a very different movement surface from the floodplains of the lower Yangtze, where wetlands and waterways dominate. The study’s framework suggests that these differences should leave distinct signatures in settlement patterns: more linear, valley-constrained arrangements in dissected terrain, and more dispersed, multidirectional patterns in open plains. Comparing accessibility metrics across such regions could help explain why proto-urban trajectories unfolded at different paces and in different forms across the Chinese Neolithic, and why some large settlements flourished for centuries while others were abandoned, possibly as shifting river courses or climatic fluctuations restructured the very accessibility surfaces that had made them attractive in the first place.

The research also connects to present-day concerns. Urban planners and geographers routinely use accessibility modeling to evaluate how road networks, transit systems, and terrain shape the growth of modern cities, and the same mathematics applies across seven millennia. Studying proto-urban settlements with these tools highlights a deep continuity: the tension between concentration and connectivity that defines urban life today was already at work when the most advanced technology available was a polished stone adze. As climate change and development pressure reshape rural landscapes in China and elsewhere, understanding how past societies responded to terrain-constrained connectivity—expanding, relocating, or reorganizing their settlements—may offer modest but genuine insight into the resilience of human communities in topographically demanding environments.

For now, the study stands as an example of how new data and new methods can reinvigorate old questions. The rise of towns and cities has been narrated for generations through excavated walls, granaries, and burials. What the accessibility approach adds is a way of seeing the invisible structure that connected those visible remains: the network of paths, slopes, and distances along which Neolithic life actually flowed. In that network, the authors argue, lie the seeds of urban organization—planted not by decree or invention, but by the patient geometry of the ground itself.

Subject of Research: The role of topographic accessibility in shaping proto-urban settlement organization in Neolithic China

Article Title: Topographic accessibility and proto-urban settlement organization in Neolithic China

Article References: Cheng, R., Yu, X., Li, D., Xu, H., Ju, G., Deng, K., & Rosés, J. (2026). Topographic accessibility and proto-urban settlement organization in Neolithic China. Nature Cities. https://doi.org/10.1038/s44284-026-00511-2

Image Credits: AI Generated

DOI: 10.1038/s44284-026-00511-2

Keywords: Neolithic China, proto-urban settlements, topographic accessibility, settlement archaeology, Nature Cities, cost-distance analysis, digital elevation models, Yellow River, Yangshao, Longshan, urban origins, computational archaeology