{"id":"80c29ffd-4228-4644-b49a-b11fb0649847","arxiv_id":"2501.15406","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":6,"one_line_summary":"A token-augmented fuzzy cognitive map is proposed for engineering design risk assessment, with a diesel engine case study showing that time delays change the final risk ranking.","lead":"This paper proposes a token-based extension of fuzzy cognitive maps to model design risks that spread in both directions and with time delays. The method is tested on a diesel engine case study, where timing changes which components are ranked as most risky.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The reported DRPNs in Table 9 are not reproducible from the iteration values in Table 7; e.g., DR2's stable cycle peaks at 0.7716 but DRPN2=0.8438, so the headline rankings lack internal support.","rationale":"The reader's weakest assumption is that the limit-cycle averaging is ad hoc. My concern is stronger and more specific: the averaging rule, as stated, cannot even be applied to Table 7 to reproduce Table 9. For DR2 and DR5 the discrepancies are several times the apparent cycle amplitude and affect the central rank ordering. Even if the averaging rule were theoretically justified, the reported numbers would still lack internal support. This does not invalidate the Token-FCM idea; the algorithm could be correct and the case-study tables erroneous. Thus the appropriate verdict remains conditional, but the conditions should include a corrected, reproducible simulation table (or code) rather than only a formal convergence criterion. I partially agree with the reader: the convergence/averaging issue is real, but the immediate load-bearing problem is the unexplained DRPN values.","tokens_in":18479,"tokens_out":9464,"duration_ms":78539,"concrete_test":"Independently recompute the DRPNs from Table 7 using the stated averaging rule. Specifically, for each row t=40,...,50 compute (i) the mean of the last three values, (ii) the mean of the last four, and (iii) the terminal value at t=50; compare to Table 9. A minimal consistency requirement is that DRPN2 should not exceed the maximum stable-cycle value (0.7716), but Table 9 reports 0.8438. If the authors cannot supply raw simulation output or code reproducing Table 9 from the described algorithm, the case study's results should be considered unverified.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section 6.2 reports that after t=40 min the RPNi values 'enter a finite cycle' and that the average of the three cycle values is used as the final DRPN, yielding the vector (0.7928, 0.8438, 0.7815, 0.8120, 0.7020, 0.7659). This vector cannot be obtained from the time series in Table 7. For DR2, all values from t=40 to t=50 lie between 0.7412 and 0.7716, so any average or the maximum is far below 0.8438; indeed DRPN2=0.8438 was only approached during the transient at t=18 (0.9009) and is never part of the steady cycle. For DR5, the steady-state values are 0.7767-0.7786, yet DRPN5=0.7020, which is actually the value of DR6 in the same rows. Likewise DRPN6=0.7659 lies far above the displayed DR6 values (≈0.7014-0.702). Moreover, the 'three cycle values' are not well-defined: DR1, DR2, and DR4 exhibit a 4-step period in Table 7 (e.g., DR1: 0.6908, 0.7872, 0.799, 0.8041). No arithmetic mean of any three or four consecutive stable values reproduces the Table 9 entries. Since the DRPN column is the basis for all comparative claims in Section 6.4 (ranking, time-delay effects, 'most impact' analysis), the demonstration of effectiveness as reported is internally unsupported.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes Token-FCM, a fuzzy cognitive map augmented with a token mechanism, to model two-way and time-delayed causal-effect risk relations in engineering design. The method uses a fuzzy-set and group-decision-based initialization to compute initial node values (RPNs), then iterates a token-based update rule to obtain dynamic risk priority numbers (DRPNs). The approach is demonstrated on a diesel engine case study and compared with a custom FMEA variant. The authors claim that Token-FCM provides more comprehensive and accurate risk assessments than static methods, and they discuss limitations including potential non-convergence in Section 7.","tokens_in":18879,"tokens_out":7949,"duration_ms":65574,"significance":"If the method were properly validated, it would address a real gap: most existing risk assessment methods cannot handle bidirectional, time-delayed causal-effect relations. The token mechanism is a creative extension of FCM, and the group-decision-based initialization is a useful contribution. However, the case study as reported is internally inconsistent and not reproducible: the reported DRPNs do not follow from the displayed iteration data, the threshold function is never specified, and the 'most impact' analysis appears degenerate. The significance of the empirical demonstration is therefore currently not established. No code, complete parameter set, or formal convergence criterion is provided.","major_comments":[{"comment":"The DRPN vector reported in Table 9 cannot be reproduced from the iteration values in Table 7. For example, the stable values of DR2 from t=40 to t=50 are 0.7716, 0.7412, 0.7662, 0.7662, 0.7716, 0.7412; neither the mean nor the maximum of these values equals the reported DRPN2=0.8438. Similarly, DRPN5=0.7020 equals the steady value of DR6, not of DR5 whose stable values are about 0.7767-0.7786, and DRPN6=0.7659 lies far above the displayed DR6 values of about 0.7014-0.702. Since the DRPN column is the basis for the rankings and for the comparative claims in Section 6.4, the demonstration of effectiveness as reported is internally unsupported.","section":"Section 6.2, Tables 7 and 9"},{"comment":"The threshold function f in Eq. (2) is stated to bound node values into [0,1], but the case-study initial values computed by Eq. (8) are negative (e.g., DR3=-1.3390, DR4=-1.5745). The specific threshold function used for the diesel engine simulation is never given; Section 3.3 mentions 'Sigmoid()' only in a small illustrative example. This makes the reported iteration values irreproducible and leaves the relationship between the negative initial RPNs and the bounded iteration values unexplained.","section":"Section 3.3 and Eq. (2)"},{"comment":"The rule 'take the average of the three cycle values' is not well-defined. In Table 7, DR1, DR2, and DR4 exhibit a four-step period (e.g., DR1: 0.6908, 0.7872, 0.799, 0.8041), not three distinct values, and no average of any three or four consecutive stable values matches the entries in Table 9. A formal convergence criterion and a precise definition of the reported 'stable' value are needed, especially because Section 7 concedes that Token-FCM can fail to converge for some time-delay assignments.","section":"Section 6.2 and Section 7"},{"comment":"The independent activation analysis in Table 8 produces nearly identical final vectors for all six initial conditions (e.g., the DR1-init and DR6-init final rows differ only in the third decimal place). The 'Most Impact DR' column in Table 9 is therefore reading noise-level differences as meaningful; the claim that 'five design risks DR1, DR3, DR4, DR5, and DR6 all have a greater influence on DR2' is not supported by the magnitude of these differences.","section":"Section 6.2, Table 8 and Table 9"}],"minor_comments":[{"comment":"There are several presentation errors in the comparison text: Table 9 lists 'Cylinder head crackin' instead of 'Cylinder head cracking', and the ranking sentence in Section 6.4.1 contains 'Camshaft failure' twice and appears internally inconsistent.","section":"Table 9 and Section 6.4.1"},{"comment":"The FMEA variant uses a nonstandard hazard index e^(O+S+D) rather than the traditional RPN product O×S×D. The text should clarify that this is a custom variant and justify why it is a fair benchmark for the comparison.","section":"Section 6.4.1, Eq. (9a)"},{"comment":"The symbol t is used both for the iteration step size in Algorithm 1 and for the range of linguistic terms in Eq. (7). This is confusing and should be disambiguated.","section":"Algorithm 1 and Eq. (7)"},{"comment":"The author name 'Docent, D.' in reference [36] appears to be a title or role rather than a person's name; the reference should be verified and corrected.","section":"Reference [36]"}],"recommendation":"major_revision","confidential_remarks":"The inconsistency between Tables 7 and 9 is the most serious issue; it is not a minor typo but affects the central empirical claim. If the authors cannot provide corrected simulation data, a precise specification of the threshold function and averaging rule, and ideally the simulation code, the paper should not be accepted. The paper's scope fits the journal, and the conceptual idea is worth pursuing, but the current demonstration is not trustworthy."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Worth knowing: Token-FCM is a real variation—tokens carry time-delay and weight information and activate node updates asynchronously. That's a concrete way to bring time delays into FCM inference, and the initialization via probabilistic linguistic term sets plus group decision is a sensible pipeline for engineering design risk. The paper is readable and the engine example is a real, non-trivial system.\n\nThe problem is the demonstration. The stress-test note is right: the DRPN values in Table 9 cannot be derived from the time series in Table 7. For instance, Table 7 shows DR2 cycling between 0.7412 and 0.7716 after t=40, but Table 9 reports DRPN2=0.8438. DR5's steady values are around 0.777, yet Table 9 gives 0.7020, which is actually DR6's value. The 'average of the three cycle values' is not specified well enough to reproduce any of these entries. Since the ranking in Section 6.4 and the time-delay comparison in Table 11 all rest on these DRPN values, the effectiveness claim is internally unsupported.\n\nTwo other soft spots. The threshold function used in the case study is never stated; Eq. 2's f is said to bound [0,1], but the initial RPN values are negative, and the paper doesn't explain how those map in. That alone is fixable, but it's needed for reproducibility. Also, the FMEA comparison in Section 6.4.1 is built from the same expert O/S/D ratings and the same arc weights, so it doesn't validate the method—it just confirms that both methods encode the same input.\n\nWhat the paper does well: the token concept is clearly explained, the pseudocode is concrete, and the authors honestly flag the possible non-convergence of time-delayed activation in Section 7. That's good practice.\n\nMy take: the contribution is a new implementation, not a new conceptual framework. The method is plausible, but the reported results fail an internal consistency check. With corrected numbers, a stated threshold function, and a comparison against generalized/dynamic FCM [6,29], it could be a useful tool. As-is, it needs major revision.","headline":"A genuinely new token-based FCM scheduling idea, but the case-study numbers don't support the headline rankings.","tokens_in":19415,"tokens_out":2597,"would_cite":false,"duration_ms":22271,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A token-augmented fuzzy cognitive map turns static design-risk models into dynamic simulators that account for time-delayed, two-way causal effects, with a diesel-engine case showing that propagation delays change which components rank as…","keywords":["risk assessment","fuzzy cognitive map","token mechanism","time delay","dynamic risk priority number","probabilistic linguistic term sets","group decision making","engineering design"],"falsifier":"Re-run the diesel-engine case study with a longer simulation horizon (for example 100 minutes instead of 50) and compare the cycle-averaged DRPN values for DR1, DR2, and DR4. If the averages change with the horizon, or if the three-value cycle repeats with a different phase, then the claimed 'stable' DRPN is an artifact of the stopping rule rather than a property of the risk dynamics; that would settle the convergence concern directly.","tokens_in":18256,"feed_emoji":"⚙️","tokens_out":5990,"duration_ms":50294,"temperature":0.7,"pith_summary":"The paper proposes Token-FCM, an extension of fuzzy cognitive maps in which small 'tokens' carry risk values along arcs and wait out a time delay before activating the target node. The aim is to make risk assessment for engineering designs capture two-way and time-delayed causal relations that static methods such as FMEA, fault trees, and ordinary FCMs ignore. The authors claim that combining this token mechanism with a group-decision initialization based on probabilistic linguistic term sets yields a dynamic risk priority number (DRPN) per component, and that the DRPN ranking is more informative than the static RPN ranking. A real diesel-engine design is used to argue that time delays change which risks dominate during operation, not just the numerical values.","feed_headline":"Tokens turn fuzzy risk maps into time-aware simulators","feed_subtitle":"A diesel-engine case shows propagation delays change which components rank as the biggest risks.","key_machinery":"The central object is the token: a packet carrying five attributes (TokenID, NodeID, NodeValue, ArcTimeDelay, ArcWeight). A node updates its value only when a token arrives, using the weighted-sum update of Eq. (2): $f(A_i) = f(A_i + \\sum_j w_j v_j)$, where $w_j$ and $v_j$ are the arc weight and starting-node value carried by token $j$. Arcs are classified into one-to-one, one-to-many, and many-to-one relations; one-to-many duplicates tokens, and many-to-one lets a node be activated several times at different arrival times. The second machinery is the initialization pipeline: expert opinions on occurrence, severity, and detection are collected as linguistic ratings, aggregated through probabilistic linguistic term sets with group decision making, converted to a fuzzy RPN via the weighted geometric-mean rule of Eq. (6), and defuzzified with Eq. (8) to give each node's initial value. Together these parts let the simulation respect time delays and bidirectional influence.","core_discovery":"Token-FCM models each design risk as a node in a fuzzy cognitive map and lets an update happen only when a token arrives after a fixed time delay, so the simulation follows a chronological trigger sequence rather than simultaneous updates. The central claim is that this produces a Dynamic Risk Priority Number (DRPN) that reflects both the static risk level (RPN, from occurrence, severity, and detection difficulty) and the two-way, time-delayed propagation of risk through the design. On the diesel-engine case, the authors show that the static ranking (piston > valve > camshaft > big-end bearing > cylinder head > fuel injector) differs from the dynamic ranking (piston > fuel injector > valve > cylinder head > camshaft > big-end bearing), and argue that the dynamic ranking is the one that matters during operation. They also claim that ignoring time delays overestimates the influence of rapid causal chains and misorders the risks.","pith_inferences":["Beyond the paper: Token-FCM is effectively a discrete-event simulation, so its convergence question can be reframed as a scheduling problem on token arrival times; a convergence guarantee might come from bounding the set of possible arrival times rather than averaging limit cycles.","Beyond the paper: a direct test of the central claim would compare DRPN-ranked components against observed failure frequencies in field data across many drilling machines; the case study shows plausibility, not predictive advantage.","Beyond the paper: the token mechanism could also be added to other graph-based risk models such as Bayesian networks or Petri nets to give them time-delayed two-way dynamics; the paper does not claim this extension, but the token design does not depend on fuzzy logic alone."],"forward_implications":["If the central claim holds, design reviews should use DRPN rankings rather than RPN rankings, because two components with the same static risk can behave very differently once propagation delays are accounted for.","The 'Most Impact DR' column tells the designer which other component's failure most amplifies each risk; for the diesel engine, piston failure (DR2) is the most influenced node, directing reliability effort to the piston and its physical and energy flow.","Time-delay modeling changes the ordering of risks: without delays the fuel injector (DR4) would appear nearly as risky as the piston, but with delays it drops below it, showing that quick-feedback loops matter more than the number of incoming influences.","The method can be transferred to other engineering designs with bidirectional, time-delayed failure relations, provided experts can agree on arc weights and delays and the simulation converges."],"supporting_citations":[{"why":"Introduces fuzzy cognitive maps, the weighted-digraph modeling base that Token-FCM extends with tokens.","marker":"[7]"},{"why":"Supplies the FCM update equation and the steady-state stopping criteria (fixed point, limit cycle, chaos) that the paper adapts to the token setting.","marker":"[27]"},{"why":"Provides the RPN = O × S × D calculation and the linguistic scales for occurrence, severity, and detection that feed the initialization.","marker":"[32]"},{"why":"Defines probabilistic linguistic term sets used to aggregate the experts' ratings in group decision making.","marker":"[34]"},{"why":"Motivates the need to consider time dynamics between a cause and an effect in fuzzy cognitive maps, which Token-FCM targets.","marker":"[6]"},{"why":"Supports the claim that standard FCM cannot simulate dynamics, the gap the token mechanism is designed to fill.","marker":"[8]"},{"why":"Supports the claim that FCM effectiveness depends on node initialization, motivating the new initialization procedure.","marker":"[9]"}],"fun_headline_variants":["Time-delayed risks change the priority order in engine design","Tokens add time to fuzzy risk maps for sharper engineering rankings","Dynamic risk ranking: piston still top, but fuel injector jumps to second","Why static risk assessment fails for complex engineering designs","Token-FCM: a time-aware twist on fuzzy cognitive maps for risk"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that when a node's value enters a repeating finite cycle of length three (DR1, DR2, DR4 in the case study), the arithmetic mean of those three values is the correct 'stable' risk index; the paper applies this averaging rule without proving that the cycle average is independent of the simulation horizon, and it concedes that some time-delay assignments never converge.","fun_headline_variants_meta":{"raw":{"variants":["Time-delayed risks change the priority order in engine design","Tokens add time to fuzzy risk maps for sharper engineering rankings","Dynamic risk ranking: piston still top, but fuel injector jumps to second","Why static risk assessment fails for complex engineering designs","Token-FCM: a time-aware twist on fuzzy cognitive maps for risk"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000709,"raw_usage":{"total_tokens":3152,"prompt_tokens":866,"completion_tokens":2286,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":482,"completion_tokens_details":{"reasoning_tokens":2200}},"tokens_in":482,"tokens_out":2286,"duration_ms":13064,"temperature":1.0,"reasoning_tokens":2200,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T14:19:45.523137+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-run the diesel-engine case study with a longer simulation horizon (for example 100 minutes instead of 50) and compare the cycle-averaged DRPN values for DR1, DR2, and DR4. If the averages change with the horizon, or if the three-value cycle repeats with a different phase, then the claimed 'stable' DRPN is an artifact of the stopping rule rather than a property of the risk dynamics; that would settle the convergence concern directly.","supporting_citations":[{"cited_title":"Fuzzy cognitive maps,","cited_arxiv_id":null,"evidence_quote":"Introduces fuzzy cognitive maps, the weighted-digraph modeling base that Token-FCM extends with tokens."},{"cited_title":"I., 2013, Fuzzy cognitive maps for applied sciences and engineering: from fundamen- tals to extensions and learning algorithms , V ol","cited_arxiv_id":null,"evidence_quote":"Supplies the FCM update equation and the steady-state stopping criteria (fixed point, limit cycle, chaos) that the paper adapts to the token setting."},{"cited_title":"Risk evaluation in failure mode and effects analysis using fuzzy weighted geomet- ric mean,","cited_arxiv_id":null,"evidence_quote":"Provides the RPN = O × S × D calculation and the linguistic scales for occurrence, severity, and detection that feed the initialization."},{"cited_title":"Probabilistic linguistic term sets in multi-attribute group decision making,","cited_arxiv_id":null,"evidence_quote":"Defines probabilistic linguistic term sets used to aggregate the experts' ratings in group decision making."},{"cited_title":"Generalised fuzzy cognitive maps: Consid- ering the time dynamics between a cause and an ef- fect,","cited_arxiv_id":null,"evidence_quote":"Motivates the need to consider time dynamics between a cause and an effect in fuzzy cognitive maps, which Token-FCM targets."},{"cited_title":"A review of fuzzy cognitive maps research during the last decade,","cited_arxiv_id":null,"evidence_quote":"Supports the claim that standard FCM cannot simulate dynamics, the gap the token mechanism is designed to fill."},{"cited_title":"A review on methods and software for fuzzy cognitive maps,","cited_arxiv_id":null,"evidence_quote":"Supports the claim that FCM effectiveness depends on node initialization, motivating the new initialization procedure."}],"review_version":1}