Mylfed 24 11 15 Freya Von Doom And Claire Roos New -

For each atomic event e at time t:

[ \textAgencyWeight(e_t) = \sigma\bigl( \mathbfw^\top \mathbfx_t + b \bigr) ]

where (\mathbfx_t) encodes lexical, syntactic, and interaction features; (\sigma) is a tanh activation to bound the output between −1 and +1.

Immersive media—ranging from narrative‑driven video games to virtual‑reality (VR) storytelling platforms—have long wrestled with the tension between authorial control and player agency (Murray, 1997; Ryan, 2001). The MylFed (My Life, Fully Expressed Dynamically) framework, first released on 24 November 2015, offered a novel solution: a procedurally generated narrative engine that adapts plot events according to a player’s in‑game decisions, while simultaneously monitoring physiological affective signals (e.g., heart‑rate variability, galvanic skin response).

Two years after its debut, Freya von Doom (computational narrative) and Claire Roos (media psychology) joined forces to address a set of open challenges identified in the original MylFed publications (von Doom, 2016; Roos, 2017): mylfed 24 11 15 freya von doom and claire roos new

The present paper introduces Dynamic Agency Modeling (DAM), a set of algorithmic extensions that answer these questions while remaining compatible with the original MylFed architecture.

| Demographic | N | Age (M ± SD) | Gender | |-------------|---|---------------|--------| | Experienced gamers (≥ 20 h/wk) | 48 | 24.3 ± 3.1 | 28 M / 20 F | | Casual players (≤ 5 h/wk) | 36 | 27.8 ± 4.5 | 18 M / 18 F |

All participants gave informed consent and were compensated £25.

| Area | Key Contributions | Relevance to MylFed | |------|-------------------|----------------------| | Procedural Narrative Generation | Façade (Mateas & Stern, 2005); Story‑Flow (Riedl & Young, 2010) | Early inspiration for branching logic | | Affective Computing in Games | Emotion Engine (Bailenson et al., 2012); Affect‑Responsive Narrative (Liu & Picard, 2014) | Basis for biosignal integration | | Player Agency Metrics | Agency Scale (Murray, 1997); Perceived Control Index (Nacke & Lindley, 2010) | Foundations for our agency evaluation | | Ethical Frameworks for Adaptive Systems | Transparency by Design (Kelley & Breazeal, 2019); AI‑Ethics for Games (Baker & O’Brien, 2021) | Guides our discussion section | For each atomic event e at time t

While these works address components of the problem space, none combine real‑time affective feedback, fine‑grained agency quantification, and transparent adaptive narration in a single, open‑source pipeline. DAM therefore fills a critical gap.

The MylFed 24‑11‑15 (MylFed‑N) project, launched on 24 November 2015, pioneered a hybrid framework that integrates procedural narrative generation, affective feedback loops, and player‑centred agency within immersive media. This paper revisits the original MylFed architecture, documents the subsequent evolution of its core algorithms, and presents the newest collaborative work of Freya von Doom and Claire Roos on Dynamic Agency Modeling (DAM). By combining von Doom’s expertise in computational narratology with Roos’s research on affective user modeling, the DAM extension delivers real‑time adaptation of story arcs to the player’s emotional state while preserving narrative coherence. Results from a controlled user study (N = 84) indicate statistically significant improvements in perceived agency (ΔM = +0.73, p < .01) and immersion (ΔM = +0.58, p < .05) over the baseline MylFed system. The paper concludes with a discussion of ethical considerations surrounding affect‑driven narrative manipulation and outlines future research directions for scaling DAM to multi‑user environments.

| Dependent Variable | Baseline (M) | DAM (M) | ΔM | t(82) | p | |--------------------|--------------|----------|----|------|---| | Perceived Agency (7‑pt) | 4.21 | 4.94 | +0.73 | 4.12 | < .001 | | Immersion (7‑pt) | 5.08 | 5.66 | +0.58 | 2.87 | < .01 | | Transparency Acceptance (5‑pt) | 2.91 | 4.02 | +1.11 | 5.03 | < .001 |

Effect sizes (Cohen’s d) ranged from 0.55 (medium) for immersion to 0.82 (large) for transparency acceptance. Qualitative feedback highlighted that participants “felt the story listened to me without taking away my freedom.” The present paper introduces Dynamic Agency Modeling (DAM)

A post‑hoc analysis revealed a moderating effect of gaming experience: experienced gamers reported higher agency gains (ΔM = 0.88) than casual players (ΔM = 0.52).

Given a stream of raw biosignals (\mathbfs_t), the AAM computes a latent representation (\mathbfzt = f\theta(\mathbfs_t)). An online loss

[ \mathcalLt = \textCE\bigl( g\phi(\mathbfz_t), \haty_t \bigr) + \lambda |\theta - \theta_0|^2 ]

is minimized, where (\haty_t) are self‑reported emotions collected via an unobtrusive on‑screen prompt (appearing no more than once every 5 minutes).