I am comparing two models with slightly different parameterizations using `loo`

and am wondering how to interpret the results when the model with higher ELPD also has more elevated Pareto k values.

```
Output of model 'Model_1':
Computed from 4000 by 6299 log-likelihood matrix
Estimate SE
elpd_loo -14585.8 84.9
p_loo 2215.2 29.9
looic 29171.6 169.8
------
Monte Carlo SE of elpd_loo is NA.
Pareto k diagnostic values:
Count Pct. Min. n_eff
(-Inf, 0.5] (good) 6231 98.9% 41
(0.5, 0.7] (ok) 63 1.0% 27
(0.7, 1] (bad) 5 0.1% 30
(1, Inf) (very bad) 0 0.0% <NA>
See help('pareto-k-diagnostic') for details.
Output of model 'Model_2':
Computed from 4000 by 6299 log-likelihood matrix
Estimate SE
elpd_loo -14131.4 87.7
p_loo 2740.7 32.6
looic 28262.8 175.5
------
Monte Carlo SE of elpd_loo is NA.
Pareto k diagnostic values:
Count Pct. Min. n_eff
(-Inf, 0.5] (good) 6113 97.0% 69
(0.5, 0.7] (ok) 171 2.7% 33
(0.7, 1] (bad) 14 0.2% 25
(1, Inf) (very bad) 1 0.0% 23
See help('pareto-k-diagnostic') for details.
Model comparisons:
elpd_diff se_diff
Model_2 0.0 0.0
Model_1 -454.4 39.7
Warning messages:
1: Found 5 observations with a pareto_k > 0.7 in model 'Model_1'. It is recommended to set 'moment_match = TRUE' in order to perform moment matching for problematic observations.
2: Found 15 observations with a pareto_k > 0.7 in model 'Model_2'. It is recommended to set 'moment_match = TRUE' in order to perform moment matching for problematic observations.
```

Does this suggest both models are mis-specified and neither should be used? Or is Model_2 still a better fit despite the high k’s?