{"doi":"10.1002/cso2.1008","title":"Are all models wrong?","abstract":"Mathematical modeling in cancer is enjoying a rapid expansion [1]. For collegial discussion across disciplines, many—if not all of us—have used the aphorism that “All models are wrong, but some are useful” [2]. This has been a convenient approach to justify and communicate the praxis of modeling. This is to suggest that the usefulness of a model is not measured by the accuracy of representation but how well it supports the generation, testing, and refinement of hypotheses. A key insight is not to focus on the model as an outcome, but to consider the modeling process and simulated model predictions as “ways of thinking” about complex nonlinear dynamical systems [3]. Here, we discuss the convoluted interpretation of models being wrong in the arena of predictive modeling. “All models are wrong, but some are useful” emphasizes the value of abstraction in order to gain insight. While abstraction clearly implies misrepresentation, it allows to explicitly define model assumptions and interpret model results within these limitations – Truth emerges more readily from error than from confusion [4]. It is thus the process of modeling and the discussions about model assumptions that are often considered most valuable in interdisciplinary research. They provide a way of thinking about complex systems and mechanisms underlying observations. Abstractions are being made in cancer biology for every experiment in each laboratory around the world. In vitro cell lines or in vivo mouse experiments are abstractions of complex adaptive evolving human cancers in the complex adaptive dynamic environment called the patient. These \"wet lab\" experiments akin to \"dry lab\" mathematical models offer confirmation or refutation of hypotheses and results, which have to be prospectively evaluated in clinical trials before conclusions can be generalized beyond the abstracted assumptions. The key for any model—mathematical, biological, or clinical—to succeed is an iterative cycle of data-driven modeling and model-driven experimentation [5, 6]. The value of such an effort lies in the insights about mechanisms that can then be attributed to the considered variables [7]. With simplified representations of a system one can learn about the emergence of general patterns, like the occurrence of oscillations, bistability, or chaos [8-10]. In this context, Alan Turing framed the purpose of a mathematical model in his seminal paper about “The chemical basis of morphogenesis” [11] with “This model will be a simplification and an idealization, and consequently a falsification. It is to be hoped that the features retained for discussion are those of greatest importance in the present state of knowledge.” For many mathematical biology models that are built to explore, test, and generate hypotheses about emerging dynamics, this remains true. “Wrong models” allow us to reevaluate our assumptions, and the lessons learned from these discussions can help formulate revised models and improve our understanding of the underlying dynamics. However, mathematical oncology models are deployed not only to simulate emergent properties of complex systems to generate, test, and refine hypotheses, but increasingly also with the intent to make predictions—often how an individual cancer patient will respond to a specific treatment [1]. For predictive modeling, the aphorism “All models are wrong” becomes awkward. In the predictive modeling arena, a useful model should not be wrong. A major hurdle in the application of predictive modeling, in general and in oncology in particular, is communication of model purpose and prediction uncertainty, and how likelihood and risks are interpreted by the end user. With limited data available about a complex adaptive evolving system, “forecasting failures” are common when events that are not represented in the data dominate the subsequent behavior (such as emergence of treatment resistance not being represented in pre-treatment dynamics). If predictive models ","journal":"Computational and Systems Oncology","year":2021,"id":167691,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":31,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.96,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":251833,"name":"Olaf Wolkenhauer","orcid":"0000-0001-6105-2937","position":1,"is_corresponding":false},{"id":278284,"name":"Heiko Enderling","orcid":"0000-0002-9696-6410","position":0,"is_corresponding":true}],"reference_count":18,"raw_metadata":null,"created_at":"2026-07-18T23:45:58.359801Z","pmid":"33585835","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}