Synthetic Social Media Influence Experimentation via an Agentic Reinforcement Learning Large Language Model Bot

Bailu Jin; Weisi Guo

REVIEW 4 major objections 6 minor 1 cited by

Synthetic Social Media Influence Experimentation via an Agentic Reinforcement Learning Large Language Model Bot

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A reinforcement-learned LLM bot learns to gain 10% of a synthetic network's followers.

desk verdict A useful simulation pipeline with released code, but the headline claims about action-space restriction and self-observation are not tested by the reported experiments. read the letter →

arxiv 2411.19635 v2 pith:ALX7JWUQ submitted 2024-11-29 cs.SI cs.CY

Bailu Jin , Weisi Guo This is my paper · ORCID

classification cs.SIcs.CY

keywords agent-basedmodelinglargelanguagemodelsreinforcementlearningQ-learningopiniondynamicssocialinfluenceleadersimulatednetwork

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a simulated online social network made of large language model agents can serve as an ethical testbed for studying opinion influence, and that a reinforcement learning agent can learn to grow its follower count inside that world. The central claims are that a Q-learning bot can gain about 10% of the total available follower population and that stable opinion-leader behavior requires two design choices: limiting the bot's action space to five predefined opinion categories and letting it observe its own follower count. If these findings hold, researchers can run controlled influence and misinformation experiments without real-world manipulation, and the framework can be extended to topics where real social data does not yet exist.

What carries the argument

The central machinery is a three-stage simulation loop — LLM post generation, opinion elicitation, and link update — with a Q-learning control loop on top. Post generation uses GPT-3.5 prompts containing the agent's prior posts and the posts of accounts it follows; opinion elicitation converts each post into a polarity score by comparing conditional perplexities of a pro sentence and a con sentence, producing a value in [0,1] that is binned into five opinion categories; link update applies a homophily rule that adds a follow link when two agents stay in the same opinion category over consecutive steps, with hand-set follow (0.8) and unfollow (0.5) probabilities. The RL bot observes the opinion states of the accounts it follows plus its own follower count, chooses one of the five opinion categories as its action, and receives the change in followers as reward. This combination matters because it lets a linguistic choice be translated, through the link rule, into a measurable network outcome that the RL can optimize.

What would settle it

Run the same RL setup with the follow probability changed to 0.5 and the unfollow probability changed to 0.8, and with the consecutive-match requirement relaxed to a single-step match; if the learned policy and the reported 10% follower gain change sharply or vanish, the result is an artifact of the link rule rather than learned influence. A complementary check is to compare the RL bot's final follower count, across many random seeds, against a non-learning bot that picks uniformly among the same five opinion categories while using the same LLM for post generation.

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Extended reading notes

Core claim

On its own terms, the discovery is a framework plus a design finding: an LLM-driven bot placed among LLM-driven users can learn, through reinforcement learning, which of five opinion stances to voice in order to maximize followers. Across eight scenario combinations — two topics, two LLM creativity settings, two link-update dynamics, and two observability settings — the authors report convergence in the reward curves and follower gains rising from 1 to 2–3 in a population of 20 agents, which they state as a 10% gain of the total available follower population. They attribute the stable emergence of this opinion-leader behavior to constraining the action space to a dictionary of real posts rather than letting the LLM generate arbitrary tweets, and to including the bot's own follower count as part of its observed state.

Load-bearing premise

The entire reward the agent learns from is produced by the rule that a user follows another user when their opinion categories match over consecutive time steps, using hand-set follow and unfollow probabilities of 0.8 and 0.5.

Editorial extensions

If this is right

If the paper's claims are correct, researchers gain a way to run ethics-safe, topic-specific experiments on how opinions form and how influence leaders emerge, using the kialo.com-derived gender and drug datasets.
The design findings give concrete guidance for building such agents: limiting the action space and adding self-observation improve convergence, while full network observability reduces variance even when the surrounding agents behave unpredictably.
A trained influence-seeking bot in this framework could be used both to simulate how a targeted persuasion campaign spreads and to test detection methods against such campaigns, a dual-use point the paper itself raises.
Because the framework uses real posts to build the action dictionary, it can be pointed at other topics where public debate exists, and the authors also propose extending it to topics like 6G acceptance where real datasets are absent.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An untested implication is that most of the 10% follower gain may come from simply choosing the opinion category the population already holds, rather than from adaptive sequencing; a fair comparison would pit the RL bot against a non-learning bot that randomizes among the same five categories, which the paper does not report.
The reward landscape is produced entirely by a homophily rule with fixed probabilities, so the result is best understood as a property of that rule; real platforms add recommendation algorithms, multi-topic ties, and bot detection, so transferring the 'stable opinion leader' conclusion to real networks would require validating the link rule against observed follow behavior.
A natural extension would be to replace the hand-set follow and unfollow probabilities with values fitted to a real social network's follow-interaction data; if the RL bot's advantage over a random baseline disappears under fitted probabilities, the claimed gain would be exposed as a simulation artifact.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First thing to know: this is a working simulation pipeline with a real topic dataset and released code, but the headline finding is not actually tested. The paper says restricting the action space and adding self-observation drive stable opinion-leader generation, yet every RL run uses the same fixed five-category dictionary and always includes the agent's follower count in the state. The 'Part-/Full-Observable' contrast changes how many other agents the target follows initially, not whether it observes itself. So the central causal claim has no experimental contrast behind it.

What is genuinely useful: the environment is concrete. LLM post generation, perplexity-based opinion scores, homophily-based link updates, and a Q-learning target agent all fit together cleanly. The action dictionary trick, where RL picks an opinion category and the LLM only renders text, is a sensible way to keep the decision space tractable. The code is on GitHub, the data is from Kialo, and the authors run two topics with multiple link dynamics and report reward curves with variance over five repetitions. As a sandbox for experimenting with opinion dynamics, it is a reasonable starting point.

The soft spots are real and they are load-bearing. The reward is follower change, and followers are created by Algorithms 2 and 3: agents start following when their opinion categories match for a few consecutive steps, with probability 0.8, and unfollow on mismatch. The RL action directly selects the opinion category. So the learned 'influence' is largely the agent discovering the homophily rule, not linguistically mediated persuasion. Section 2.3 claims the method does not prescribe an influence mechanism, but the link-update rule is exactly such a prescription. The baseline is asserted in prose ('statistical flat line') rather than measured. The '10% follower gain' is an artifact of this setup, not a general result.

None of this kills the framework as a simulation tool, but it means the paper's second contribution, the discovery about action-space restriction and self-observation, is unsupported as stated. Fixing it would require real ablations: a larger or unrestricted action space, a state without the agent's own follower count, and a baseline that is actually computed rather than described.

Who should read it: people working on LLM-agent social simulations, especially those building RL-controlled bots. It is not ready as-is; the claims need to be scaled back or the experiments need to deliver the missing contrasts. A serious referee should see it, because the infrastructure is real and the flaws are fixable. If I were handling it, I would send it out with the expectation of major revision, and I would ask specifically for the ablations.

Referee Report

4 major / 6 minor

Summary. The manuscript introduces a simulated social media environment in which LLM-driven agents produce topic-specific posts, have their opinions elicited via perplexity scores, and establish or break follow links based on opinion-category similarity. A target agent is trained with Q-learning to choose among five predefined opinion categories in order to maximize its follower count. Experiments are run on Gender and Drug topics across eight scenarios that vary LLM decoding settings, follow/unfollow dynamics, and initial observability, reporting rewards of roughly one to three followers in a 20-agent population. The paper claims that restricting the action space and incorporating self-observation are key to stable opinion-leader generation, and that RL achieves a gain of 10% of the total available follower population.

Significance. The combination of LLM post generation, perplexity-based opinion measurement, and Q-learning in a synthetic social network is a potentially useful prototype for ethically exploring topic-specific opinion dynamics, and the paper provides detailed algorithms and a public code repository. If the central claims were established, the framework could be a valuable testbed for studying influence processes with real-data alignment. However, the experimental design never varies the two factors named in the headline claims, the baseline is asserted rather than measured, and the reward mechanism is a direct consequence of the hand-coded homophily rule. The current evidence is therefore not sufficient to support the paper's stated contributions.

major comments (4)

[§1.6, §5.11, §5.14] The central claim that limiting the action space and incorporating self-observation are key factors is not tested by any ablation. All eight scenarios use the same five-category action dictionary (§4.7) and the same state vector that always includes the target's follower count (§4.6); the Part-/Full-Observable contrast in §5.14 varies whether the target initially follows one or all agents, not whether self-observation is present. Without conditions that vary action-space size and the presence of self-observation, the experiments cannot support this headline contribution.
[§3.23–3.24, Algorithms 2–3, §4.5] The reward is the change in follower count, and follow links are created or removed only when opinion categories match or differ across consecutive steps, with hand-set probabilities (follow 0.8 and unfollow 0.5, per §5.14). Since the RL action directly selects the opinion category, the follower reward is a direct consequence of this hand-coded homophily rule rather than evidence of linguistically mediated influence. This also contradicts §2.3, which states that the method does not prescribe a particular mechanism for how influence spreads; Algorithms 2 and 3 prescribe precisely such a mechanism.
[§5.16] The baseline is asserted, not measured. The statement that a no-RL agent "would be a statistical flat line with no followers gained over time" is presented without a corresponding experiment, and no random-policy, fixed-action, or random-initial-condition comparator is reported. Consequently, the reported 10% follower gain (§5.15) cannot be attributed to RL, and the convergence of the learning curves (§5.17) only shows that training stabilized rather than that the agent outperformed a baseline.
[§5.17, §6.4] The claim that convergence of reward learning curves indicates that "the RL algorithm successfully identified optimal solutions" is not justified. Q-learning convergence guarantees apply to stationary single-agent Markov decision processes, whereas this is a non-stationary multi-agent environment in which all other agents' posts and opinions evolve partly in response to the target agent. No convergence theorem, optimality check, or comparison with exhaustive policy evaluation is provided, so the word "optimal" in the conclusions is unsupported.

minor comments (6)

[§3.23, Algorithms 2–3] The parameter num_same, which controls the number of consecutive matching opinion categories required for a follow or unfollow event, is not specified in Table 3 or in the main text; its value and choice should be reported.
[Table 3, Algorithm 4] The relationship between epsilon and exploration rate is unclear, and Algorithm 4 refers to "a policy derived from Q" without defining the exploration schedule; please specify the exact action-selection rule.
[§1.4, Figure 3] The claim that the simulation result "aligns with the polarity distribution of the actual dataset" is supported only by qualitative inspection of the plotted distributions; a quantitative comparison would strengthen the validation.
[Figures 6–7] The captions of the learning curves should state the number of repeated runs and explicitly define the shaded areas as variance or confidence intervals, so that the reported stability can be judged.
[§4.6, Figure 2, Table 3] The state description lists "self-observation" as a component, but the formal state definition in §4.6 only mentions followed agents' opinion states and the target's follower count; please clarify what the self-observation component is.
[Various] There are several typographical issues, including "Table.2 shows the our setting" after Table 2 and "filed experiments" in §2.2; a careful proofread is needed.

Circularity Check

1 steps flagged · score 6.0 of 10

The '10% follower gain' is a direct consequence of the hand-coded homophily link rule acting on the RL action itself; the 'key factors' claim is not tested by the reported scenarios.

self definitional [Sections 3.23-3.24 (Algorithms 2-3), 4.5, 4.7, 5.15]
"To establish the 'Following' relationship between users, we adopt the premise that individuals inclined to follow users who express similar opinions… if two users consistently align in the same opinion category over a few consecutive steps, they are considered likely to follow each other… The available action space consists of five predefined opinion polarity categories: strong con, con, neutral, pro, and strong pro… The followers gained (reward) rises from 1 to 2 or 3 in different cases in a total agent population of 20."

The reward (change in follower count) is produced entirely by Algorithms 2 and 3: a follow link is created when the target's opinion category matches another agent's for num_same consecutive steps and is deleted on category mismatch. The RL action is exactly that opinion category, so the objective function is the same categorical-matching predicate that generates the reward. The reported '10% of the total available follower population' therefore counts how many agents the hand-written rule attaches to the chosen category; it is a redescription of the rule's output, not an independent measurement of linguistic or social influence. No external or baseline comparison calibrates this number.

full rationale

The main quantitative claim reduces to the environment's own link-update rule: because the action is the opinion category and followers are awarded for category matches, the RL result is a direct consequence of the homophily premise adopted in Section 3.23, not an externally calibrated finding. The paper's other headline claim, that limiting the action space and incorporating self-observation are key factors, is not supported by the reported experiments: all eight scenarios in Section 5.14 use the same five-category action dictionary (Section 4.7) and always include the target's own follower count in the state (Section 4.6); the 'Part-/Full-Observable' contrast changes the initial follow list, not self-observation. Section 5.16's baseline is asserted in prose ('it would be a statistical flat line') rather than measured, so the learned advantage has no calibrated comparison. Section 2.3's claim that 'we do not prescribe a particular mechanism in how influence is spread' is also contradicted by the explicit category-matching follow/unfollow rule. The authors' own limitation statement (Section 6.5) acknowledges that the 'rule-based simulated environment cannot fully replicate' real social dynamics; this mitigates the epistemic claim but does not remove the by-construction status of the follower gain. No load-bearing self-citation chain was found. Overall, the central '10% gain' is a construction-level consequence, giving partial circularity.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The simulation rests on several hand-set assumptions: follow links are created and destroyed by opinion-similarity rules with chosen probabilities, opinion scores come from LLM perplexity against two fixed sentences, and the RL reward is the follower count produced by those same rules. None of these assumptions is validated against real behavioral data, so the central finding is conditional on them.

free parameters (7)

follow rate threshold = 0.8
Probability of creating a follow link when opinion categories match over consecutive steps (Algorithm 2, Section 5.14).
unfollow rate = 0.5 (Follow-Unfollow) or 0 (Follow)
Probability of breaking a follow link when opinion categories differ (Algorithm 3, Section 5.14).
num_same = not specified in text
Number of consecutive same-category steps required to trigger follow or unfollow in Algorithms 2 and 3; value not given in the article.
opinion category thresholds = Tables 5 and 6
Polarity score boundaries chosen to ensure even distribution across five categories (Section 5.3, Appendix C).
Q-learning learning rate alpha = 0.01
Tabular Q-learning update step size (Table 3).
Q-learning discount factor gamma = 0.99
Discount factor in Q-value update (Table 3).
LLM decoding temperature and top_p = Narrow 0.1/0.5; Creative 1.4/0.95
GPT-3.5 decoding parameters for agent post generation (Table 4), hand set to define narrow versus creative agent behavior.

assumptions (5)

domain assumption Users follow others who express similar opinions (homophily).
Section 3.23: 'we adopt the premise that individuals inclined to follow users who express similar opinions' (citing Zhou et al. 2009); this premise determines how follower reward is generated and is never empirically validated in the paper.
domain assumption Conditional perplexity of an LLM measures the opinion polarity of a post.
Section 3.20-3.22: opinion score is defined from PPL of two fixed pro/con sentences (Eqs. 2-4); no calibration against human labels or downstream validation is reported.
domain assumption LLM-generated posts and opinion updates realistically model human discussion.
Section 3.10-3.12 uses GPT-3.5 to generate posts from prompts; Section 6.5 acknowledges the environment 'cannot fully replicate' real social dynamics.
domain assumption Random selection from an opinion dictionary is a valid proxy for a linguistic influence strategy.
Section 4.8: after selecting an opinion category, the target agent randomly chooses a tweet from that category; this means the 'language' is only category-level, and tweet-level content is sampled at random.
ad hoc to paper Q-learning convergence in this non-stationary multi-agent environment implies an optimal policy.
Section 5.17 interprets convergence of averaged reward curves as 'optimal solutions'; standard Q-learning convergence guarantees assume stationarity, which does not hold when other agents' behaviors co-evolve.

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Cite this review

Pith. "Pith review of Synthetic Social Media Influence Experimentation via an Agentic Reinforcement Learning Large Language Model Bot." pith.science (2026). https://pith.science/paper/ALX7JWUQ

@misc{pith2026241119635,
  author       = {Pith},
  title        = {Pith review of: Synthetic Social Media Influence Experimentation via an Agentic Reinforcement Learning Large Language Model Bot},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ALX7JWUQ}},
  note         = {Machine review of arXiv:2411.19635}
}

read the original abstract

Understanding the dynamics of public opinion evolution on online social platforms is crucial for understanding influence mechanisms and the provenance of information. Traditional influence analysis is typically divided into qualitative assessments of personal attributes (e.g., psychology of influence) and quantitative evaluations of influence power mechanisms (e.g., social network analysis). One challenge faced by researchers is the ethics of real-world experimentation and the lack of social influence data. In this study, we provide a novel simulated environment that combines agentic intelligence with Large Language Models (LLMs) to test topic-specific influence mechanisms ethically. Our framework contains agents that generate posts, form opinions on specific topics, and socially follow/unfollow each other based on the outcome of discussions. This simulation allows researchers to observe the evolution of how opinions form and how influence leaders emerge. Using our own framework, we design an opinion leader that utilizes Reinforcement Learning (RL) to adapt its linguistic interaction with the community to maximize its influence and followers over time. Our current findings reveal that constraining the action space and incorporating self-observation are key factors for achieving stable and consistent opinion leader generation for topic-specific influence. This demonstrates the simulation framework's capacity to create agents that can adapt to complex and unpredictable social dynamics. The work is important in an age of increasing online influence on social attitudes and emerging technologies.

Figures

Figures reproduced from arXiv: 2411.19635 by the authors.

**Figure 2.** RL Loop. Reward: change of number of followers at each time step. State: 1)the current opinion states [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Distribution of polarity value. Simulated Environment Initialisation 5.1 The initiation of our simulated environment begins with the creation of initial posts, for which we use the dataset released by Betz (2022). The dataset is crawled from debating platform kialo.com, comprises various posts structured around specific topics. For our simulation, we select posts related to the topic ’Gender’ and topic ’Drug’. The d… view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Opinion Evolution Visualization on Narrow setting and Creative setting [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 5.** Figure 5: Standard Deviation of Opinion on Narrow setting and Creative setting [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

**Figure 6.** Figure 6: Learning Curves of RL in Gender case, with different language model settings, link update methods, [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]

**Figure 7.** Figure 7: Learning Curves of RL in Drug case, with different language model settings, link update methods, and [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

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Works this paper leans on

18 extracted references · 16 canonical work pages · cited by 1 Pith paper

[1]

, " * write output.state after.block = add.period write newline

ENTRY address author booktitle chapter doi edition editor eid howpublished institution journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 'mid.sentence...

work page
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

work page
[3]

Betz, G. (2021). Natural-language multi-agent simulations of argumentative opinion dynamics. arXiv preprint arXiv:2104.06737

arXiv 2021
[4]

& Markopoulos, I

Briguglio, L., Nesse, P.-J., Di Giglio, A., Occhipinti, C., Durkin, P. & Markopoulos, I. (2021). Business value and social acceptance for the validation of 5g technology. In 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), (pp. 132--137). IEEE

work page 2021
[5]

D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A. et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877--1901

work page 2020
[6]

Hamilton, S. (2023). Blind judgement: Agent-based supreme court modelling with gpt. arXiv preprint arXiv:2301.05327

arXiv 2023
[7]

C., Rand, D., Lewis, K., Norton, M

Jackson, J. C., Rand, D., Lewis, K., Norton, M. I. & Gray, K. (2017). Agent-based modeling: A guide for social psychologists. Social Psychological and Personality Science, 8(4), 387--395

work page 2017
[8]

& Wu, H.-N

Luo, B., Liu, D. & Wu, H.-N. (2017). Adaptive constrained optimal control design for data-based nonlinear discrete-time systems with critic-only structure. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2099--2111

work page 2017

Show all 18 references

[9]

& Losada, D

Meyer, S., Elsweiler, D., Ludwig, B., Fernandez-Pichel, M. & Losada, D. E. (2022). Do we still need human assessors? prompt-based gpt-3 user simulation in conversational ai. In Proceedings of the 4th Conference on Conversational User Interfaces, (pp. 1--6)

2022
[10]

A., Veness, J., Bellemare, M

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G. et al. (2015). Human-level control through deep reinforcement learning. nature, 518(7540), 529--533

2015
[11]

OpenAI (2022). Gpt-3.5. https://openai.com/

2022
[12]

S., Popowski, L., Cai, C., Morris, M

Park, J. S., Popowski, L., Cai, C., Morris, M. R., Liang, P. & Bernstein, M. S. (2022). Social simulacra: Creating populated prototypes for social computing systems. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, (pp. 1--18)

2022
[13]

& Crestani, F

Sekuli \'c , I., Aliannejadi, M. & Crestani, F. (2022). Evaluating mixed-initiative conversational search systems via user simulation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, (pp. 888--896)

2022
[14]

& Morales, A

Sert, E., Bar-Yam, Y. & Morales, A. J. (2020). Segregation dynamics with reinforcement learning and agent based modeling. Scientific reports, 10(1), 11771

2020
[15]

J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M. et al. (2016). Mastering the game of go with deep neural networks and tree search. nature, 529(7587), 484--489

2016
[16]

Thorndike, E. L. (1898). Animal intelligence: An experimental study of the associative processes in animals. The Psychological Review: Monograph Supplements, 2(4), i
[17]

Watkins, C. J. & Dayan, P. (1992). Q-learning. Machine learning, 8, 279--292

1992
[18]

& Zhang, C

Zhou, H., Zeng, D. & Zhang, C. (2009). Finding leaders from opinion networks. In 2009 IEEE International Conference on Intelligence and Security Informatics, (pp. 266--268). IEEE

2009

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

, " * write output.state after.block = add.period write newline

ENTRY address author booktitle chapter doi edition editor eid howpublished institution journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 'mid.sentence...

work page

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

work page

[3] [3]

Betz, G. (2021). Natural-language multi-agent simulations of argumentative opinion dynamics. arXiv preprint arXiv:2104.06737

arXiv 2021

[4] [4]

& Markopoulos, I

Briguglio, L., Nesse, P.-J., Di Giglio, A., Occhipinti, C., Durkin, P. & Markopoulos, I. (2021). Business value and social acceptance for the validation of 5g technology. In 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), (pp. 132--137). IEEE

work page 2021

[5] [5]

D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A. et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877--1901

work page 2020

[6] [6]

Hamilton, S. (2023). Blind judgement: Agent-based supreme court modelling with gpt. arXiv preprint arXiv:2301.05327

arXiv 2023

[7] [7]

C., Rand, D., Lewis, K., Norton, M

Jackson, J. C., Rand, D., Lewis, K., Norton, M. I. & Gray, K. (2017). Agent-based modeling: A guide for social psychologists. Social Psychological and Personality Science, 8(4), 387--395

work page 2017

[8] [8]

& Wu, H.-N

Luo, B., Liu, D. & Wu, H.-N. (2017). Adaptive constrained optimal control design for data-based nonlinear discrete-time systems with critic-only structure. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2099--2111

work page 2017